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Sari, Julia Purnama, Aan Erlansari, and Endina Putri Purwandari. "Identifikasi Citra Digital Kura-Kura Sumatera Dengan Perbandingan Ekstraksi Fitur GLCM Dan GLRLM Berbasis Web." Pseudocode 8, no. 1 (2021): 66–75. http://dx.doi.org/10.33369/pseudocode.8.1.66-75.
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Kura-kura merupakan hewan yang sangat mudah dikenali karena mempunyai bentuk tubuh yang khas. Ciri khas yang dimiliki oleh kura-kura adalah adanya karapaks yang sering disebut dengan cangkang. Dalam mengidentifikasi kura-kura tidak bisa sembarangan, dibutuhkan seorang pakar yang benar-benar paham dengan spesies tersebut. Identifikasi keanekaragaman spesies kura-kura sumatera melalui pengolahan citra digital ini menggunakan metode ekstraksi fitur tekstur berbasis website. Salah satu cara mengidentifikasi jenis kura-kura yaitu dengan menggunakan sistem identifikasi secara otomatis berbasis pemrosesan citra digital. Pada penelitian ini dilakukan perbandingan dua ekstraksi ciri yaitu Gray Level Co-Occurrence Matrix (GLCM) dan Gray Level Run Length Matrix (GLRLM). Ekstraksi ciri GLCM dan GLRLM yang dilakukan pada penelitian ini menggunakan sudut 0°, 45°, 90°, 135°. Hasil Penelitian menunjukkan bahwa hasil akurasi identifikasi dengan menggunakan ekstraksi ciri GLRLM lebih baik dibandingkan GLCM. Hasil Akurasi tertinggi pada GLRLM 79,5% sementara dengan GLCM menghasilkan akurasi sebesar 75%.Kata Kunci: Identifikasi, Kura-kura, Karapaks, Gray Level Run length Matrix, Gray Level Co-Occurrence Matrix.
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Elbashier, Mona E., Suhaib Alameen, Caroline Edward Ayad, and Mohamed E. M. Gar-Elnabi. "Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 8. http://dx.doi.org/10.23956/ijarcsse.v7i7.88.
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This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.
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Ruchi, Luhadiya* Prof. Dr. Anagha Khedkar. "IRIS RECOGNITION USING GRAY LEVEL RUN LENGTH MATRIX AND KNN CLASSIFIER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 424–31. https://doi.org/10.5281/zenodo.203815.
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Biometric devices are great tools for the security. Iris is a very unique identifying characteristic amongst all human biometric traits. In the proposed system, the biometric authentication system using iris recognition is presented. In this iris image is preprocessed then image localized with the Hough transform, normalized using Daugman’s rubbersheet model finally image sharpening is used with the morphological toggle filter. Feature extraction is done using Gray Level Run Length Matrix (GLRLM) technique with 0 directions and classification is done using multiclass KNN. This system is evaluated on CASIA database and it gives 92.66% accuracy.
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Purwandari, Endina Putri, Rachmi Ulizah Hasibuan, and Desi Andreswari. "Identifikasi Jenis Bambu Berdasarkan Tekstur Daun dengan Metode Gray Level Co-Occurrence Matrix dan Gray Level Run Length Matrix." Jurnal Teknologi dan Sistem Komputer 6, no. 4 (2018): 146–51. http://dx.doi.org/10.14710/jtsiskom.6.4.2018.146-151.
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Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.
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S, Sanjayprabu, Sathish Kumar R, Saeid Jafari, and Karthikamani R. "On Performance Analysis Of Diabetic Retinopathy Classification." ELCVIA Electronic Letters on Computer Vision and Image Analysis 22, no. 2 (2024): 12–25. http://dx.doi.org/10.5565/rev/elcvia.1677.
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This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-order PDE is used. Using OCT retinal fundus images, the most effective feature extraction method is identified.
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Lu, Hsueh-Ju, Chao-Yu Shen, Yu-Wei Chiu, et al. "Predictors of the survival for platinum-refractory head and neck squamous cell carcinoma by using contrast-enhanced magnetic resonance imaging." Journal of Clinical Oncology 40, no. 16_suppl (2022): 6038. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.6038.
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6038 Background: Platinum-refractory head and neck squamous cell carcinoma (HNSCC) was a poor prognosis. Under the treatment of immune checkpoint therapy, overall survival (OS) was still only 7.7 months. Moreover, the prognostic factors were unclear. Methods: For enrolled platinum-refractory HNSCC patients, the presented images of the axial, coronal, and sagittal planes from T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging (T1 CE MRI) were independently reviewed by two physicians. First, the most representative characteristics of the recurrent tumor on the three planes were manually identified, and the intersection coordinate was calculated as a circle center by computerized methods. Then, the center delineated a region of interest (ROI) with a radius of 25 pixels on each representative slice and excluded the air areas. Descriptive features and four groups of texture features (Gray Level Co-occurrence Matrix [GLCM], Gray Level Run Length Matrix [GLRLM], Gray Level Size Zone Matrix [GLSZM], and Neighbouring Gray Tone Difference Matrix [NGTDM]) were derived from the ROI to depict MRI intensities' distribution and heterogeneity. Results: A total of 30 patients were retrospectively enrolled, including immune checkpoint therapy (N = 19) and cetuximab-based chemotherapy (N = 11) as front-line therapy. Median OS was 7.0 months with 17 death events during follow-up. The area size of ROI was not significantly associated with the death events. However, High Gray-level Run Emphasis (HGRE) and Short-Run High Gray-level Emphasis (SRHGE) of GLRLM as well as High Gray-level Zone Emphasis (HGZE) and Short-Zone High Gray-level Emphasis (SZHGE) of GLSZM were significant to predict the event of death. The area under the curve (AUC) of HGRE, SRHGE, HGZE, and SZHGE were 0.747 (P = 0.023), 0.842 (P = 0.002), 0.819 (P = 0.003), and 0.833 (P = 0.002) at discretization levels of 16, respectively. These features also significantly differed OS of platinum-refractory HNSCC (Table). Conclusions: Four texture features of GLRLM and GLSZM depicted the fragmentation of extracellular volume of the recurrent tumor on T1 CE MRI and significantly correlated with the outcomes of platinum-refractory HNSCC. However, future warranted studies were needed. 1-year survival Hazard ratio (95% CI) High Gray-level Run Emphasis (HGRE) 54.5% vs. 9.7% 2.218(0.629-7.826) Short-Run High Gray-level Emphasis (SHRGE) 65.9% vs. 0.0% 4.254(1.213-14.915) High Gray-level Zone Emphasis (HGZE) 75.0% vs. 0.0% 8.741(1.134-67.397) Short-Zone High Gray-level Emphasis (SZHGE) 100.0% vs. 0.0% 45.726(0.714-2927.085)
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Pintaric, Karlo, Vladka Salapura, Ziga Snoj, Andrej Vovk, Mojca Bozic Mijovski, and Jernej Vidmar. "Assessment of short-term effect of platelet-rich plasma treatment of tendinosis using texture analysis of ultrasound images." Radiology and Oncology 57, no. 4 (2023): 465–72. http://dx.doi.org/10.2478/raon-2023-0054.
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Abstract Background Computer-aided diagnosis (i.e., texture analyses) tools are becoming increasingly beneficial methods to monitor subtle tissue changes. The aim of this pilot study was to investigate short-term effect of platelet rich plasma (PRP) treatment in supraspinatus and common extensor of the forearm tendinosis by using texture analysis of ultrasound (US) images as well as by clinical questionnaires. Patients and methods Thirteen patients (7 male and 6 female, age 36–60 years, mean age 51.2 ± 5.2) were followed after US guided PRP treatment for tendinosis of two tendons (9 patients with lateral epicondylitis and 4 with supraspinatus tendinosis). Clinical and US assessment was performed prior to as well as 3 months after PRP treatment with validated clinical questionnaires. Tissue response in tendons was assessed by using gray level run length matrix method (GLRLM) of US images. Results All patients improved of tendinosis symptoms after PRP treatment according to clinical questionnaires. Almost all GLRLM features were statistically improved 3 months after PRP treatment. GLRLM-long run high gray level emphasis (LRLGLE) revealed the best moderate positive and statistically significant correlation after PRP (r = 0.4373, p = 0.0255), followed by GLRLM-low gray level run emphasis (LGLRE) (r = 0.3877, p = 0.05). Conclusions Texture analysis of tendinosis US images was a useful quantitative method for the assessment of tendon remodeling after minimally invasive PRP treatment. GLRLM features have the potential to become useful imaging biomarkers to monitor spatial and time limited tissue response after PRP, however larger studies with similar protocols are needed.
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Mulyono, Agus, Muthmainnah Muthmainnah, and Nuralfin Anripa. "STROKE SEVERITY ANALYSIS THROUGH CT-SCAN IMAGE TEXTURE ANALYSIS OF THE BRAIN WITH GRAY LEVEL RUN LENGTH MATRIX METHOD." Jurnal Neutrino:Jurnal Fisika dan Aplikasinya 16, no. 2 (2024): 53–59. http://dx.doi.org/10.18860/neu.v16i2.26261.
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The condition of a stroke is when the blood supply to the brain is disrupted due to a blockage (ischemicstroke) or rupture of a blood vessel (hemorrhagic stroke). This condition causes certain areas of thebrain to be deprived of the supply of oxygen and nutrients resulting in the death of brain cells. Thisstudy aims to determine the process of ischemic stroke assistance and hemorrhagic analysis through CTScan image texture GLRLM brain method with the classification method using discriminant analysisand determine the level of accuracy. In this study there are 3 stages, namely preprocessing, learningstages and testing stages. The results of the assessment of stroke in the ischemic and hemorrhagiccategories through texture analysis of CT scan images using the GLRLM brain method with aclassification accuracy of 100%.
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Górski, Kamil, Marta Borowska, Elżbieta Stefanik, et al. "Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses’ Incisor Teeth Affected by the EOTRH Syndrome." Sensors 22, no. 8 (2022): 2920. http://dx.doi.org/10.3390/s22082920.
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Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses’ dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation.
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Maiti, Sudeepta, Shailesh Nayak, Karthikeya D. Hebbar, and Saikiran Pendem. "Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study." F1000Research 13 (March 14, 2024): 91. http://dx.doi.org/10.12688/f1000research.146052.2.
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Background Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). Methods This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Results Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. Conclusions MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.
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Aarthi D. "Optimizing Medical Image Classification Using Diverse Feature Extraction Methods for Brain Tumor." Panamerican Mathematical Journal 35, no. 2s (2024): 340–52. https://doi.org/10.52783/pmj.v35.i2s.2638.
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Detecting brain tumor early and accurately is crucial for effective treatment and better patient outcomes. Three feature extraction methods are used in this study: Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Histogram Features (GLHF)—to classify MRI images of the brain. GLCM measures the relationship between pixel intensities to capture texture information, while GLRLM finds patterns of pixels with similar gray levels, showing areas of texture. The histogram method summarizes the overall intensity in the image, highlighting differences between normal and abnormal areas. A Support Vector Machine (SVM), a classifier designed to differentiate between brain tissue affected by tumors and normal tissue, processes these retrieved features. Using a typical MRI dataset, the research assesses how well each feature extraction method supports accurate classification by the SVM. By comparing their performance, the study identifies which technique is best for distinguishing between healthy and tumor regions in the brain. This analysis offers valuable insights into improving brain tumor detection, potentially benefiting clinical diagnosis and treatment planning.
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Bo, Yi, Junli Xie, Jianguo Zhou, Shikun Li, Yuezhan Zhang, and Zhenjiang Zhou. "Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network." Contrast Media & Molecular Imaging 2021 (July 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/4095433.
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The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) influence characteristics of cerebral infarction in critically ill patients based on the convolutional neural network (CNN). Fifty patients with cerebral infarction were enrolled and examined by MRI. Besides, a CNN artificial intelligence system was established for learning and training. The features were extracted from the MRI image results of the patients, and then, the data were calculated by computer technology. The gray-level cooccurrence matrix (GLCM) of T1-weighted images was 0.872 ± 0.069; the reasonable prediction (ALL) result was 0.766 ± 0.112; the gray-level run-length matrix (GLRLM) was 0.812 ± 0.101; the multigray-level area size matrix (MGLSZM) result was 0.713 ± 0.104; and the result of gray-scale area size matrix (GLSZM) was 0.598 ± 0.099. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of enhanced T1-weighted images were 0.710 ± 0.169, 0.742 ± 0.099, 0.778 ± 0.096, 0.801 ± 0.104, and 0.598 ± 0.099, respectively. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of T2-weighted images were 0.780 ± 0.096, 0.798 ± 0.087, 0.888 ± 0.086, 0.768 ± 0.112, and 0.767 ± 0.100, respectively. In short, the image diagnosis method that could reduce the subjective visual judgment error to a certain extent was found by analyzing the characteristics of MRI images of critically ill patients with cerebral infarction based on CNN.
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Qiu, Wu, Feng Xiao, Xin Yang, Mao Lin Ye, Yu Chi Ming, and Ming Yue Ding. "Application of Fuzzy Enhancement in the Diagnosis of Liver Cancer from Ultrasound Images." Applied Mechanics and Materials 195-196 (August 2012): 493–97. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.493.
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Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM). The results show that the fuzzy enhancement algorithm can improve classification accuracy of normal liver, liver cancer and Hemangioma from B mode ultrasound images for three classifiers. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.
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Preetha, K., and Dr S.K. Jayanthi. "GLCM and GLRLM based Feature Extraction Technique in Mammogram Images." International Journal of Engineering & Technology 7, no. 2.21 (2018): 266. http://dx.doi.org/10.14419/ijet.v7i2.21.12378.
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A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammogram helps for early diagnosis before showing symptoms of cancer. The aim of this paper is to extract the various features of pre-processed mammogram images to improve the performance of the diagnosis, which helps the radiologists in reducing the false positive predictions. Mammogram images are pre-processed using hybrid filter MAX_AVM. Shape, Intensity, Gray Level Co-occurrence Matrix and Gray Level Run-Length Matrix features that help to represent the various classes of objects are extracted and used as inputs to the classifier. The classifier helps to classify the mammogram images into a normal or abnormal pattern. Experiments were conducted on MIAS database. The result shows that the combination of GLCM and GLRLM features are efficient and achieved the maximum classification accuracy rate when compared to other features.
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Lee, Heechang, Taeyoung Yoon, Chaeyun Yeo, et al. "Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features." Applied Sciences 11, no. 20 (2021): 9460. http://dx.doi.org/10.3390/app11209460.
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The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.
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V, Vidhya, U. Raghavendra, Anjan Gudigar, et al. "Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques." Informatics 9, no. 1 (2022): 4. http://dx.doi.org/10.3390/informatics9010004.
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Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.
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Domino, Małgorzata, Marta Borowska, Anna Trojakowska, et al. "The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing." Animals 12, no. 2 (2022): 195. http://dx.doi.org/10.3390/ani12020195.
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Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.
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Park, Inae, Misuk Lee, Jeevan Shivakumar, and Sungjae Park. "Radiomic survival prediction with liver metastases in colorectal cancer." Journal of Clinical Oncology 42, no. 16_suppl (2024): e15584-e15584. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e15584.
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e15584 Background: The survival outcome of colorectal cancer depends upon staging and the presence of metastases. Nonetheless, there isn’t a reliable prediction model for overall survival (OS) specifically tailored to patients with colorectal cancer who have liver metastases. Consequently, this study aims to find radiomic features that can be utilized to predict OS of colorectal cancer with liver metastases. Methods: We utilized the Colorectal Liver Metastases data set in The Cancer Imaging Archive (TCIA). Overall, 197 patients with colorectal cancer metastatic to the liver were identified. Semi-automatic segmentation of liver metastases was used to extract radiomic features including Gray-Level Co-occurrence (glcm), Gray-Level Run Length Matrix (glrlm), Gray-Level Size Zone Matrix (glszm), Gray-Level Dependence Matrix (gldm), and Neighborhood Gray-Tone Difference Matrix (ngtdm). 3D Slicer was used to extract radiomic features. We tested a Cox proportional hazards model in each radiomics feature to predict OS. Results: Total 107 radiomic features were extracted from pre-segmented liver metastases on patients’ Computed Tomography images. Among them, original shape sphericity, original glcm correlation, original gldm DependenceEntropy, and original glrlm RunEntropy were statistically significant for the prediction of OS, with a hazard ratio of -2.16 (CI 0.01-0.92; p value 0.04), 1.54 (CI 1.5-14.0; p value 0.006), 0.51 (CI 1.0-2.5; p value 0.02), and 0.52 (CI 1.0-2.6; p value 0.03), respectively. Conclusions: Our data analysis indicates crucial radiomic features that hold promise for predicting the clinical outcome of patients with colorectal cancer who have liver metastases. Future research with a larger sample size is warranted for the external validation of our results.
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Telaumbanua, Kristian, Sudarto Sudarto, Fitri Butar-Butar, and Putri Shania Bilqis. "Identifikasi Sampah Berdasarkan Tekstur Dengan Metode GLCM dan GLRLM Menggunakan Improved KNN." Explorer 1, no. 2 (2021): 45–52. http://dx.doi.org/10.47065/explorer.v1i2.94.
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Inorganic garbage is a type of garbage which takes a long time to decompose naturally, and it is important to recycle this type of garbage to avoid it stacking up in the environment. Before the recycle process, the inorganic garbage will be grouped which needs an identification of its form of material. The digital image processing can be used to create a system which could identify the form of material the inorganic garbage is by analyzing the features. In this research, the feature that will be used is the texture feature. The texture feature will be extracted using the Gray Level Co-Occurrence Matrix, and Gray Level Run Length Matrix method. And for the material of garbage identification will use the Improved KNN classification method. The results of the test by using 50 images as data testing from 5 different material of garbage which is cardboard, glass, metal, paper, and plastic type of garbage have the highest mean of accuracy 90,4% by using the GLRLM method with 135° angle of extraction. Meanwhile the accuracy when combining the extraction methods, which is adding up the value of GLCM and GLRLM, have the highest mean of accuracy 88% with 0° angle of extraction
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Kausar, Fizhan, and Ramamurthy B. "Coati Optimization Algorithm for Detecting Pediatric Kidney Abnormalities using Ultrasound Images." International Research Journal of Multidisciplinary Scope 06, no. 02 (2025): 874–84. https://doi.org/10.47857/irjms.2025.v06i02.03236.
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This study aimed to classify pediatric ultrasound images as normal or abnormal by identifying the optimal number of image texture features for analysis and developing an effective classification system using selected features. The experiment identified a successful feature selection and classification algorithm with a good performance. This study introduced a new approach for computer-assisted ultrasound image classification. Initially, a Gaussian median filter enhances the image quality and removes noise. For feature extraction, various features, including first-order derivatives, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Dependence Matrix (GLDM), Gray Level Size Matrix (GLSZM), and Neighbouring gray tone difference matrix (NGTDM), were extracted using the Pyrandiomics Python package. The Coati optimization algorithm (COA) was employed as a feature selection technique. The Classification was performed using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Naï ve Bayes (NB), and Extreme Gradient Boosting (XG-Boost) algorithms. Therefore, this study proposed a new machine learning classifier, the Extreme Gradient Neighborhood classifier (XGNC), using NB, KNN, and XG-Boost, with a classification accuracy of 97.91%, which outperformed the other classifiers mentioned in the study. The results indicated that the optimal feature selection and classifier choice yielded the most accurate computer-aided diagnosis of kidney abnormalities.
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Hilal, Suha Raheem, Hussain S. Hasan, and Ali M. Hasan. "Magnetic Resonance Imaging Breast Scan Classification based on Texture Features and Long Short-Term Memory Model." NeuroQuantology 19, no. 7 (2021): 41–47. http://dx.doi.org/10.14704/nq.2021.19.7.nq21082.
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The aim of study is building new program for processing MRI images using MATLAB and to investigate different breast MRI detection algorithms that inform normal and abnormal scans of MRI. In this research an algorithm is proposed to extract texture feature and inform normal and abnormal scans of MRI. First, the MRI scans are pre- processed by image enhancement, intensity normalization, background segmentation and detection of mirror symmetry of breast. Second, the proposed gray level co- occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods are used to extract texture features from MRI T2-weighted and STIR images. Finally, these features are classified into normal and abnormal by using long short term memory (LSTM) model. The research will be validated using 326 datasets that downloaded from cancer imaging archive (TCIA). The achieved classification accuracy was 98.80%.
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Ekert, Kaspar, Clemens Hinterleitner, Karolin Baumgartner, Jan Fritz, and Marius Horger. "Extended Texture Analysis of Non-Enhanced Whole-Body MRI Image Data for Response Assessment in Multiple Myeloma Patients Undergoing Systemic Therapy." Cancers 12, no. 3 (2020): 761. http://dx.doi.org/10.3390/cancers12030761.
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Identifying MRI-based radiomics features capable to assess response to systemic treatment in multiple myeloma (MM) patients. Retrospective analysis of whole-body MR-image data in 67 consecutive stage III MM patients (40 men; mean age, 60.4 years). Bone marrow involvement was evaluated using a standardized MR-imaging protocol consisting of T1w-, short-tau inversion recovery- (STIR-) and diffusion-weighted-imaging (DWI) sequences. Ninety-two radiomics features were evaluated, both in focally and diffusely involved bone marrow. Volumes of interest (VOI) were used. Response to treatment was classified according to International Myeloma Working Group (IMWG) criteria in complete response (CR), very-good and/or partial response (VGPR + PR), and non-response (stable disease (SD) and progressive disease (PD)). According to the IMWG-criteria, response categories were CR (n = 35), VGPR + PR (n = 19), and non-responders (n = 13). On apparent diffusion coefficient (ADC)-maps, gray-level small size matrix small area emphasis (Gray Level Size Zone (GLSZM) small area emphasis (SAE)) significantly correlated with CR (p < 0.001), whereas GLSZM non-uniformity normalized (NUN) significantly (p < 0.008) with VGPR/PR in focal medullary lesions (FL), whereas in diffuse involvement, 1st order root mean squared significantly (p < 0.001) correlated with CR, whereas for VGPR/PR Log (gray-level run-length matrix (GLRLM) Short Run High Gray Level Emphasis) proved significant (p < 0.003). On T1w, GLRLM NUN significantly (p < 0.002) correlated with CR in FL, whereas gray-level co-occurrence matric (GLCM) informational measure of correlation (Imc1) significantly (p < 0.04) correlated with VGPR/PR. For diffuse myeloma involvement, neighboring gray-tone difference matrix (NGTDM) contrast and 1st order skewness were significantly associated with CR and VGPR/PR (p < 0.001 for both). On STIR-images, CR correlated with gray-level co-occurrence matrix (GLCM) Informational Measure of Correlation (IMC) 1 (p < 0.001) in FL and 1st order mean absolute deviation in diffusely involved bone marrow (p < 0.001). VGPR/PR correlated at best in FL with GSZLM size zone NUN (p < 0.019) and in all other involved medullary areas with GLSZM large area low gray level emphasis (p < 0.001). GLSZM large area low gray level emphasis also significantly correlated with the degree of bone marrow infiltration assessed histologically (p = 0.006). GLCM IMC 1 proved significant throughout T1w/STIR sequences, whereas GLSZM NUN in STIR and ADC. MRI-based texture features proved significant to assess clinical and hematological response (CR, VPGR, and PR) in multiple myeloma patients undergoing systemic treatment.
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Jawli, Adel, Ghulam Nabi, Zhihong Huang, Abeer J. Alhusaini, Cheng Wei, and Benjie Tang. "Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from Ultrasound Shear-Wave Elastography." Cancers 17, no. 8 (2025): 1358. https://doi.org/10.3390/cancers17081358.
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Introduction: Artificial intelligence (AI) is increasingly utilized for texture analysis and the development of machine learning (ML) techniques to enhance diagnostic accuracy. ML algorithms are trained to differentiate between normal and malignant conditions based on provided data. Texture feature analysis, including first-order and second-order features, is a critical step in ML development. This study aimed to evaluate quantitative texture features of normal and prostate cancer tissues identified through ultrasound B-mode and shear-wave elastography (SWE) imaging and to develop and assess ML models for predicting and classifying normal versus malignant prostate tissues. Methodology: First-order and second-order texture features were extracted from B-mode and SWE imaging, including four reconstructed regions of interest (ROIs) from SWE images for normal and malignant tissues. A total of 94 texture features were derived, including features for intensity, Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Dependence Length Matrix (GLDLM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Five ML models were developed and evaluated using 5-fold cross-validation to predict normal and malignant tissues. Results: Data from 62 patients were analyzed. All ROIs, except those derived from B-mode imaging, exhibited statistically significant differences in features between normal and malignant tissues. Among the developed models, Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes (NB) demonstrated the highest performance across all ROIs. These models consistently achieved strong predictive accuracy for classifying normal versus malignant tissues. Gray Pure SWE and Gray Reconstructed images Provided the highest sensitivity and specificity in PCa prediction by 82%, 90%, and 98%, 96%, respectively. Conclusions: Texture analysis with machine learning on SWE-US and reconstructed images effectively differentiates malignant from benign prostate lesions, with features like contrast, entropy, and correlation playing a key role. Random Forest, SVM, and Naïve Bayes showed the highest classification performance, while grayscale reconstructions (GPSWE and GRRI) enhanced detection accuracy.
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Kalbhor, Madhura, and Swati Shinde. "ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams." Diagnostics 13, no. 6 (2023): 1103. http://dx.doi.org/10.3390/diagnostics13061103.
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Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams. In this paper, ColpoClassifier, a hybrid framework for the classification of cervigrams, is proposed, which consists of feature extraction followed by classification. This paper uses a Gray-level co-occurrence matrix (GLCM), a Gray-level run length matrix (GLRLM), and a histogram of gradients (HOG) for feature extraction. These features are combined to form a feature fusion vector of the form GLCM + GLRLM + HOG. The different machine learning classifiers are used for classification by using individual feature vectors as well as feature fusion vectors. The dataset used in this paper is compiled by downloading images from the WHO website. Two variants of this dataset are created, Dataset-I contains images of the aceto-whitening effect, green filter, iodine application, and raw cervigram while Dataset-II only contains images of the aceto-whitening effect. This paper presents the classification performance on all kinds of images with the individual as well as hybrid feature fusion vector and concludes that hybrid feature fusion vectors on aceto-whitening images have given the best results.
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García, G., A. Tapia, and Blas M. De. "Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution." Computer Methods and Programs in Biomedicine 115, no. 1 (2014): 11–19. https://doi.org/10.1016/J.CMPB.2014.03.004.
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An abdominal aortic aneurysm (AAA) is a localized abnormal enlargement of the abdominal aorta with fatal consequences if not treated on time. The <em>endovascular aneurysm repair</em> (EVAR) is a minimal invasive therapy that reduces recovery times and improves survival rates in AAA cases. Nevertheless, post-operation difficulties can appear influencing the evolution of treatment. The objective of this work is to develop a pilot computer-supported diagnosis system for an automated characterization of EVAR progression from CTA images. The system is based on the extraction of texture features from post-EVAR thrombus aneurysm samples and on posterior classification. Three conventional texture-analysis methods, namely the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), the gray level difference method (GLDM), and a new method proposed by the authors, the run length matrix of local co-occurrence matrices (RLMLCM), were applied to each sample. Several classification schemes were experimentally evaluated. The ensembles of a <em>k</em>-nearest neighbor (<em>k</em>-NN), a multilayer perceptron neural network (MLP-NN), and a support vector machine (SVM) classifier fed with a reduced version of texture features resulted in a better performance (<em>A</em><sub><em>z</em></sub> = 94.35 ± 0.30), as compared to the classification performance of the other alternatives.
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Kayathri, K., and Dr K. Kavitha. "CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification." International Journal of Electrical and Electronics Research 12, no. 2 (2024): 669–81. http://dx.doi.org/10.37391/ijeer.120245.
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This research proposes an integrated approach for automated diabetic retinopathy (DR) diagnosis, leveraging a combination of machine learning and deep learning techniques to extract features and perform classification tasks effectively. Through preprocessing of retinal images to enhance features and mitigate noise, two distinct methodologies are employed: machine learning feature extraction, targeting texture features like Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM), and deep learning feature extraction, utilizing pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, or Inception. Following feature extraction, various classifiers, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, are trained on the extracted features for DR classification. Alternatively, deep learning classifiers like CNNs or recurrent neural networks (RNNs) may be trained directly on the extracted features or on raw images. This comprehensive framework shows promising potential to improve the accuracy and efficiency of diabetic retinopathy (DR) diagnosis, enabling timely intervention and management of this vision-threatening condition.
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Xu, Sendren Sheng-Dong, Chun-Chao Chang, Chien-Tien Su, and Pham Quoc Phu. "Classification of Liver Diseases Based on Ultrasound Image Texture Features." Applied Sciences 9, no. 2 (2019): 342. http://dx.doi.org/10.3390/app9020342.
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This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions.
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Orhan, Kaan, Lukas Driesen, Sohaib Shujaat, Reinhilde Jacobs, and Xiangfei Chai. "Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies." BioMed Research International 2021 (July 5, 2021): 1–11. http://dx.doi.org/10.1155/2021/6656773.
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The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A radiomics platform was used to extract (Huiying Medical Technology Co., Ltd., China) imaging features of TMJ pathologies, condylar bone changes, and disc displacements. Thereafter, different machine learning (ML) algorithms and logistic regression were implemented on radiomic features for feature selection, classification, and prediction. The following radiomic features included first-order statistics, shape, texture, gray-level cooccurrence matrix (GLCM), gray-level run length matrix (GLRLM), and gray-level size zone matrix (GLSZM). Six classifiers, including logistic regression (LR), random forest (RF), decision tree (DT), k -nearest neighbors (KNN), XGBoost, and support vector machine (SVM) were used for model building which could predict the TMJ pathologies. The performance of models was evaluated by sensitivity, specificity, and ROC curve. KNN and RF classifiers were found to be the most optimal machine learning model for the prediction of TMJ pathologies. The AUC, sensitivity, and specificity for the training set were 0.89 and 1, while those for the testing set were 0.77 and 0.74, respectively, for condylar changes and disc displacement, respectively. For TMJ condylar bone changes Large-Area High-Gray-Level Emphasis, Gray-Level Nonuniformity, Long-Run Emphasis Long-Run High-Gray-Level Emphasis, Flatness, and Volume features, while for TMJ disc displacements Average Intensity, Sum Average, Spherical Disproportion, and Entropy features, were selected. This study has proposed a machine learning model by KNN and RF analysis on TMJ MR images, which can be used to classify condylar changes and TMJ disc displacements.
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Ou, Xuejin, Jian Wang, Ruofan Zhou, et al. "Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma." Contrast Media & Molecular Imaging 2019 (February 25, 2019): 1–9. http://dx.doi.org/10.1155/2019/4507694.
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Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p=0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p≤0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P<0.001); SUVstd, 2.04 versus 0.88 g/ml (P=0.001); SUVmax, 10.69 versus 4.76 g/ml (P=0.001); SUVpeak, 9.15 versus 2.78 g/ml (P<0.001); TLG, 42.24 versus 9.90 (P<0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.
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Piccolo, Claudia Lucia, Marina Sarli, Matteo Pileri, et al. "Radiomics for Predicting Prognostic Factors in Breast Cancer: Insights from Contrast-Enhanced Mammography (CEM)." Journal of Clinical Medicine 13, no. 21 (2024): 6486. http://dx.doi.org/10.3390/jcm13216486.
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Objectives: To evaluate the correlation between radiomic features extracted from contrast-enhanced mammography (CEM) tumor lesions and peritumoral background with prognostic factors in breast cancer (BC). Methods: In this retrospective, single-center study, 134 women with histologically confirmed breast cancer underwent CEM examination. Radiomic features were extracted from manually segmented lesions and lesion contours were automatically delineated using PyRadiomics. The extracted features were categorized into seven classes: First-order Features, Shape Features (2D), Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). Histological examination assessed tumor type, grade, receptor structure (ER, PgR, HER2), Ki67 index, and lymph node involvement. Pearson correlation and multivariate regression were applied to evaluate associations between radiomic features and prognostic factors. Results: Significant correlations were found between First-order Features and prognostic factors such as ER, PgR, and Ki67 (p < 0.05). GLCM-based texture features showed strong associations with Ki67 and HER2 (p < 0.01). Radiomic features from peritumoral regions, especially shape and GLSZM metrics, were significantly correlated with Ki67 and lymph node involvement. Conclusions: Radiomic analysis of both tumor and peritumoral regions offers significant insights into BC prognosis. These findings support the integration of radiomics into personalized diagnostic and therapeutic strategies, potentially improving clinical decision making in BC management.
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Purnama Yanti, Christina, and I. Gede Andika. "HSV image classification of ancient script on copper Kintamani inscriptions using GLRCM and SVM." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (2020): 94–99. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.94-99.
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The problem of inscription physical damage as one of the historical heritages can be overcome using an image processing technique. The purpose of this study is to design a segmentation application for ancient scripts on inscriptions to recognize the character patterns on the inscriptions in digital form. The preprocessing was carried out to convert images from RGB to HSV. The application used the gray level run length matrix (GLRLM) to extract texture features and the support vector machine (SVM) method to classify the results. The inscription image segmentation was carried out through the pattern detection process using the sliding window method. The application obtained 88.32 % of accuracy, 0.87 of precision, and 0.94 of sensitivity.
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Suvarchala, P. V. L., and S. Srinivas Kumar. "Feature Set Fusion for Spoof Iris Detection." Engineering, Technology & Applied Science Research 8, no. 2 (2018): 2859–63. http://dx.doi.org/10.48084/etasr.1859.
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Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.
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Suvarchala, P. V. L., and S. Srinivas Kumar. "Feature Set Fusion for Spoof Iris Detection." Engineering, Technology & Applied Science Research 8, no. 2 (2018): 2859–63. https://doi.org/10.5281/zenodo.1257853.
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Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database was 100% while it was 95.63% and 88.83% with IITD spoof iris databases respectively.
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Tamanna, Nafisa Kaisar, Azmal Kabir Sarker, Sabuz Paul, et al. "Correlation Of Radiomics Features from Thyroid Planar Scan with 99mTc Uptake– A Preliminary Study." Bangladesh Journal of Nuclear Medicine 27, no. 2 (2025): 169–72. https://doi.org/10.3329/bjnm.v27i2.79188.
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Background: Use of radiomics features (RF) is being increasingly applied for the computer aided risk stratification of malignancy in thyroid nodules, using two- and three-dimensional imaging data. We explored the characteristic of radiomic features extracted from 99mTc thyroid planar scan (TTPS) images. Patients and methods: Anterior planar images from patients who underwent TTPS with quantification of percent 99mTc-uptake (%TU) were analyzed in LIFEX software to generate RF data from two regions of interest (ROI): right lobe and left lobe per patient. Correlation of the RF from multiple categories were checked against the TU of each lobe. After discovering RF with significant correlation with demographic and clinical variables, the RF in each category with smallest p-value (i.e. the best correlation) were shortlisted. Results: A total of 140 RF were extracted from 44 lobes in 22 consecutive patients. Among the RF, 21 of 32 morphological, 21 of 23 intensity based, 24 of 29 intensity histogram derived, 22 of 24 Gray-Level Co-occurrence Matrix (GLCM) based, 8 of 11 Gray-Level Run-Length Matrix (GLRLM) based, four of five Neighboring Gray Tone Difference Matrix (NGTDM) based and 10 of 16 Gray Level Size Zone (GLSZM) based, showed significant correlation with %TU (p < 0.05). The RF with lowest p-values in each category were, morphological integrated intensity (R = 0.89, p = 3.59e-16), intensity-based energy (R = 0.94, p = 5.48e-21), intensity histogram maximum histogram gradient (R = 0.9, p = 1.56e-16), GLCM angular second moment (R 0.92, p = 5.13e-19), GLRLM long runs emphasis (R = 0.95, p = 3.36e-23), NGTDM coarseness (R = -0.59, p = 2.59e-05), GLSZM large zone emphasis (R = 0.96, p = 8.06e-25), GLSZM large zone high grey level emphasis (R = 0.96, p = 6.46e-25), GLSZM zone size variance (R = 0.96, p = 6.8e-25). Conclusions: Correlations of RF with %TU indicate the possibility of finding RF as biomarkers for nodular and generalized parenchymal thyroid diseases from TTPS images as well as possible usefulness of these RF in the prediction of disease outcome. Bangladesh J. Nuclear Med. 27(2): 169-172, 2024
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Prasla, Shopnil, Daniel Moore-Palhares, Daniel Dicenzo, et al. "Predicting Tumor Progression in Patients with Cervical Cancer Using Computer Tomography Radiomic Features." Radiation 4, no. 4 (2024): 355–68. https://doi.org/10.3390/radiation4040027.
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The objective of this study was to evaluate the effectiveness of utilizing radiomic features from radiation planning computed tomography (CT) scans in predicting tumor progression among patients with cervical cancers. A retrospective analysis was conducted on individuals who underwent radiotherapy for cervical cancer between 2015 and 2020, utilizing an institutional database. Radiomic features, encompassing first-order statistical, morphological, Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Dependence Matrix (GLDM) features, were extracted from the primary cervical tumor on the CT scans. The study encompassed 112 CT scans from patients with varying stages of cervical cancer ((FIGO Staging of Cervical Cancer 2018): 24% at stage I, 47% at stage II, 21% at stage III, and 10% at stage IV). Of these, 31% (n = 35/112) exhibited tumor progression. Univariate feature analysis identified three morphological features that displayed statistically significant differences (p < 0.05) between patients with and without progression. Combining these features enabled a classification model to be developed with a mean sensitivity, specificity, accuracy, and AUC of 76.1% (CI 1.5%), 70.4% (CI 4.1%), 73.6% (CI 2.1%), and 0.794 (CI 0.029), respectively, employing nested ten-fold cross-validation. This research highlights the potential of CT radiomic models in predicting post-radiotherapy tumor progression, offering a promising approach for tailoring personalized treatment decisions in cervical cancer.
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Tabari, Azadeh, Brian D’Amore, Janice Noh, Michael S. Gee, and Dania Daye. "Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer." Exploration of Targeted Anti-tumor Therapy 5, no. 1 (2024): 74–84. http://dx.doi.org/10.37349/etat.2024.00205.
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Aim: To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). Methods: From 2007–2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Results: Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5–103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. Conclusions: MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.
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Sasikumar, P., and K. Venkatachalapathy. "An Ensemble of Feature Extraction with Whale Optimization Algorithm for Content Based Image Retrieval System." Journal of Computational and Theoretical Nanoscience 17, no. 12 (2020): 5386–98. http://dx.doi.org/10.1166/jctn.2020.9432.
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In recent days, content based image retrieval (CBIR) becomes a hot research area, which aims to determine the relevant images to the query image (QI) from the available large sized database. This paper presents an optimal hybrid feature extraction with similarity measure (OHFE-SM) for CBIR. Initially, histogram equalization of images takes place as a preprocessing step. Then, texture, shape and color features are extracted. The texture features include Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is extracted, where the optimal number of features will be chosen by whale optimization algorithm (WOA). Afterwards, the shape feature extraction takes place by Crest lines and color feature extraction process will be carried out using Quaternion moments. Finally, Euclidean distance will be applied as a similarity measure to determine the distance among the feature vectors exist in the database and QI. The images with higher similarity index will be considered as relevant images and is retrieved from the database. A detailed experimental validation takes place against Corel10K dataset. The simulation results showed that the proposed OHFE-SM model has outperformed the existing methods with the higher average precision of 0.915 and recall of 0.780.
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ROSATI, SAMANTA, KRISTEN MARIKO MEIBURGER, GABRIELLA BALESTRA, U. RAJENDRA ACHARYA, and FILIPPO MOLINARI. "CAROTID WALL MEASUREMENT AND ASSESSMENT BASED ON PIXEL-BASED AND LOCAL TEXTURE DESCRIPTORS." Journal of Mechanics in Medicine and Biology 16, no. 01 (2016): 1640006. http://dx.doi.org/10.1142/s0219519416400066.
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Aim of this paper is to develop an automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors. A database of 200 longitudinal ultrasound images of carotid artery is used. One-hundred images with Intima-Media Thickness (IMT) value higher than 0.8[Formula: see text]mm are considered as high risk. Six different rectangular pixel neighborhoods were considered: four areas centered on the selected element, with sizes [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] pixels, and two noncentered areas with sizes [Formula: see text] pixels upwards and downwards. We have extracted various texture descriptors (31 based on the co-occurrence gray level matrix, 13 based on the spatial gray level dependence matrix, and 20 based on the gray level run length matrix (GLRLM) from neighborhood. We have used Quick Reduct Algorithm to select 12 most discriminant features from extracted 211 features. Each pixel is then assigned to the vessel lumen, to the intima-media complex, or to the adventitia by using an integrated system of three feed-forward neural networks. The boundaries between the three regions are used to estimate the IMT value. The texture features associated with GLRLM are found to be clinically most significant. We have obtained an overall classification accuracy of 79.5%, sensitivity of 87%, and specificity of 72%. We observed a unique classification pattern between low risk and high risk images: in the latter ones, a considerable number of pixels of the intima–media complex ([Formula: see text]) was classified as belonging to the adventitia. This percentage is statistically higher than that of low risk images ([Formula: see text]; [Formula: see text]). Locally extracted and pixel-based descriptors are able to capture the inner characteristics of the carotid wall. The presence of misclassified pixels in the intima–media complex is associated to higher cardiovascular risk.
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Phukon, Nilutpal, Ksh Robert Singh, Takhellambam Sonamani Singh, et al. "Quality Assessment of Rice Using Convolution Neural Networks and Other Machine Learning Techniques- A Comparative Study." Current Agriculture Research Journal 12, no. 2 (2024): 762–72. http://dx.doi.org/10.12944/carj.12.2.21.
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Rice holds its position as the most widely cultivated crop worldwide, its demand steadily rising alongside population growth. The precise identification of grains, especially wheat and rice, holds significant importance as their ultimate utilization depends on the quality of grains prior to processing. Traditionally, grain identification tasks have been predominantly manual, relying on experienced grain inspectors and consuming considerable time. However, this manual classification process is susceptible to variations influenced by individual perception, given the subjective nature of human image interpretation. Consequently, there is an urgent need for an automated recognition system capable of accurately identifying grains under diverse environmental conditions, necessitating the application of digital image processing techniques. In this study, we focus on grading five distinct varieties of rice based on their quality, employing a range of convolution neural networks (CNN) namely, Efficientnetb0, Googlenet, MobileNetV2, Resnet50, Resnet101, and ShuffleNet. The performance of CNN towards identification and grading of rice grain is also compared to that of other parametric and Non-parametric classifiers namely, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (K-NN), Naive Bayes (NB), and Back Propagation Neural Network (BPNN) using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) based texture features. The image dataset comprises five grades of rice, each containing 100 images, resulting in a comprehensive collection of 500 samples for analysis. It is observed that, Convolution neural networks can grade five different qualities of rice with highest accuracy of 64.4% in case of GoogleNet. Results show that, rice grading using texture features performs better with highest accuracy of 99.2% (using GLCM) and 93.4% (using GLRLM).
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Koda, Eriko, Tsuneo Yamashiro, Rintaro Onoe, et al. "CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer." PLOS ONE 15, no. 12 (2020): e0243181. http://dx.doi.org/10.1371/journal.pone.0243181.
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Objectives To investigate the potential of computed tomography (CT)-based texture analysis and elastographic data provided by endobronchial ultrasonography (EBUS) for differentiating the mediastinal lymphadenopathy by sarcoidosis and small cell lung cancer (SCLC) metastasis. Methods Sixteen patients with sarcoidosis and 14 with SCLC were enrolled. On CT images showing the largest mediastinal lymph node, a fixed region of interest was drawn on the node, and texture features were automatically measured. Among the 30 patients, 19 (12 sarcoidosis and 7 SCLC) underwent endobronchial ultrasound transbronchial needle aspiration, and the fat-to-lesion strain ratio (FLR) was recorded. Texture features and FLRs were compared between the 2 patient groups. Logistic regression analysis was performed to evaluate the diagnostic accuracy of these measurements. Results Of the 31 texture features, the differences between 11 texture features of CT ROIs in the patients with sarcoidosis versus patients with SCLC were significant. Among them, the grey-level run length matrix with high gray-level run emphasis (GLRLM-HGRE) showed the greatest difference (P<0.01). Differences between FLRs were significant (P<0.05). Logistic regression analysis together with receiver operating characteristic curve analysis demonstrated that the FLR combined with the GLRLM-HGRE showed a high diagnostic accuracy (100% sensitivity, 92% specificity, 0.988 area under the curve) for discriminating between sarcoidosis and SCLC. Conclusion Texture analysis, particularly combined with the FLR, is useful for discriminating between mediastinal lymphadenopathy caused by sarcoidosis from that caused by metastasis from SCLC.
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Atlas, Lawrence Livingston Godlin, and Kumar Parasuraman. "Effective Approach to Classify and Segment Retinal Hemorrhage Using ANFIS and Particle Swarm Optimization." Journal of Intelligent Systems 27, no. 4 (2018): 681–97. http://dx.doi.org/10.1515/jisys-2016-0354.
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Abstract The main objective of this study is to progress the structure and segment the images from hemorrhage recognition in retinal fundus images in ostensible. The abnormal bleeding of blood vessels in the retina which is the membrane in the back of the eye is called retinal hemorrhage. The image folders are deliberated, and the filter technique is utilized to decrease the images specifically adaptive median filter in our suggested proposal. Gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM) and Scale invariant feature transform (SIFT) feature skills are present after filtrating the feature withdrawal. After this, the organization technique is performed, specifically artificial neural network with fuzzy interface system (ANFIS) method; with the help of this organization, exaggerated and non-affected images are categorized. Affected hemorrhage images are transpired for segmentation procedure, and in this exertion, threshold optimization is measured with numerous optimization methods; on the basis of this, particle swarm optimization is accomplished in improved manner. Consequently, the segmented images are projected, and the sensitivity is great when associating with accurateness and specificity in the MATLAB platform.
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Agus, Maman Abadi, Urwatul Wutsqa Dhoriva, and Ningsih Nurlia. "Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 4 (2021): 1273–83. https://doi.org/10.12928/telkomnika.v19i4.20398.
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In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level cooccurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis.
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Cullison, Kaylie, Garrett Simpson, Danilo Maziero, et al. "NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY." Neuro-Oncology 23, Supplement_6 (2021): vi142. http://dx.doi.org/10.1093/neuonc/noab196.554.
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Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.
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Rahmawan, Rizki Dwi, Umi Salamah, and Ery Permana Yudha. "Hybrid features to classify lung tumor using machine learning." Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 2 (2025): 447–60. https://doi.org/10.35882/ijeeemi.v7i2.101.
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A lung tumor is an abnormal mass of cells inside a body. As a benign tumor is unproblematic, but a malignant tumor is cancerous because it can travel across the body and interfere with its surrounding tissue. Detecting these cancerous cells in the lung is important because delayed detection may hamper effective treatment options, leading to a lower survival rate. However, classifying tumor malignancy is highly dependent on the knowledge and experience of the radiologist. This study combines texture-based features extracted from lung Computed Tomography Scan (CT Scan) images such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRLM), Gray Level Size-zone Matrix (GLSZM), and Haralick Features aims to create a lung tumor classification system. This research contributes by creating an efficient and reliable system through Relief-F feature selection that uses features with the highest weight in rank that are able to differentiate classes of tumor malignancy and help medical professionals diagnose tumors more early in the treatment. As a comparison, several conventional machine learning classifiers, including SVM RBF, KNN, RF, DT, and XGBoost, were utilized to evaluate classifier performance. The result showed that the accuracy of the proposed hybrid features with a random forest classifier was the most performing approach with an evaluation score of accuracy of 99.55%, precision of 99.55%, recall of 99.55%, and F1-Score of 99.54%. Furthermore, accuracy among other classifiers was also higher than 90%. Proofing the selected features retain essential class information, demonstrating the study’s applicability in developing automated lung tumor classification systems from CT scans.
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Shen, Wei-Chih, Ying-Hsiang Chou, Meng-Wei Chung, Guo-Zhi Wang, Jang-Ming Lee, and Yi-Han Liao. "Feasibility of pretreatment FDG PET radiomics in predicting circumferential margin involvement for esophageal cancer after neoadjuvant concurrent chemoradiotherapy." Journal of Clinical Oncology 42, no. 23_suppl (2024): 203. http://dx.doi.org/10.1200/jco.2024.42.23_suppl.203.
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203 Background: Involvement of circumferential resection margin (CRM) proves to be a valuable factor in predicting prognosis for patients with esophageal cancer. This study aimed to explore the feasibility of using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in predicting CRM involvement for patient received neoadjuvant concurrent chemoradiotherapy (CCRT). Methods: We retrospectively enrolled patients with esophageal cancer who received neoadjuvant CCRT followed by radical surgery between October 2013 and July 2023. All patients received FDG PET/CT examinations before CCRT. The maximum standardized uptake value (SUVmax) within a tumor was identified, and the metabolic tumor volume (MTV) was calculated by using a fixed SUV threshold method (50 % of SUVmax). Descriptive features and five groups of radiomic features (Gray Level Co-occurrence Matrix [GLCM], Gray Level Dependence Matrix [GLDM], Gray Level Run Length Matrix [GLRLM], Gray Level Size Zone Matrix [GLSZM], and Neighboring Gray Tone Difference Matrix [NGTDM]) were further calculated to describe the heterogeneity of the FDG uptakes within MTV. The CRM involvement status was defined by the Royal College of Pathologists (RCP). Tumor presence at or within 1 mm of the cut margin was considered CRM positive. Finally, we used the area under the receiver operating characteristic curve (AUC) to evaluate the predictive performance of radiomic features for CRM. The significance level was set to 0.05. Results: A total of 71 cases of esophageal tumors, encompassing data from 70 patients were included. Of the 70 patients, 94% were male with a mean age 58 years across the entire cohort. Sixteen tumors were proved as CRM positive (16/71, 22.5%). Finally, 2 descriptive features and 10 radiomic features were selected that can effectively predict CRM status. These ten radiomic features exhibit robust predictive power for CRM status, demonstrating significance across various discretization bin number. The best performing feature is Skewness (AUC=0.716, p-value<0.0001). Conclusions: This preliminary result revealed that CRM involvement for esophageal cancer tumors could be predicted by using radiomic features. Future studies are warranted for clinical application. [Table: see text]
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Liu, Peng, Haitao Zhu, Haibin Zhu, et al. "Predicting survival for hepatic arterial infusion chemotherapy of unresectable colorectal liver metastases: Radiomics analysis of pretreatment computed tomography." Journal of Translational Internal Medicine 10, no. 1 (2022): 56–64. http://dx.doi.org/10.2478/jtim-2022-0004.
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Abstract Objective Hepatic arterial infusion chemotherapy (HAIC) is an effective treatment for advanced unresectable colorectal cancer liver metastases (CRLM). This study was conducted to predict the efficacy of HAIC in patients with unresectable CRLM by radiomics methods based on pretreatment computed tomography (CT) examinations and clinical data. Materials and Methods A total of 63 patients were included in this study (41 in the training group and 22 in the validation group). All these patients underwent CT examination before HAIC. During the follow-up period, CT scans and laboratory examinations were performed regularly. Eighty-five radiological features were extracted from the regions of interest (ROIs) of CT images using the PyRadiomics program. The t-test and correlation were applied to select features. These features were analyzed using LASSO-Cox regression, and a linear model was developed to predict overall survival (OS). Results After reducing features by t-test and correlation test, seven features remained. After LASSO-Cox cross-validation, four features remained at λ = 0.232. They were gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM), and the location of the primary tumor. The C-index was 0.758 in the training group and 0.743 in the test group. Nomograms predicting 1-, 2-, and 3-year survival were established. Conclusion Our study demonstrates that a radiomics approach based on pretreatment CT texture analysis has the ability to predict early the outcome of HAIC in patients with advanced unresectable colorectal cancer with a high degree of accuracy and feasibility.
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Sujjada, Alun, Muchtar Ali Setyo Yudono, Somantri, Fabrobi Fazlur Ridha, Abdul Haris Kuspranoto, and Irma Saraswati. "Ekstraksi Ciri Berbasis GLRLM untuk Klasifikasi Katarak pada Pembuluh Darah dan Optic Disc pada Citra Fundus Smartphone." Jurnal RESTIKOM : Riset Teknik Informatika dan Komputer 7, no. 1 (2025): 111–22. https://doi.org/10.52005/restikom.v7i1.437.
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Deteksi dini katarak merupakan langkah krusial dalam upaya pencegahan kebutaan, terutama di negara berkembang dengan keterbatasan akses terhadap layanan kesehatan mata. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi katarak berbasis citra retina yang diambil menggunakan kamera fundus smartphone, dengan fokus pada area optic disc sebagai objek utama analisis. Proses pengolahan mencakup pra-pengolahan citra, segmentasi optic disc, ekstraksi ciri tekstur menggunakan metode Gray Level Run Length Matrix (GLRLM), dan klasifikasi menggunakan Backpropagation Neural Network (BPNN). Dataset yang digunakan terdiri dari 60 citra retina, terbagi menjadi empat kelas: normal, katarak mild, medium, dan severe. Hasil evaluasi menunjukkan bahwa akurasi pelatihan mencapai 100%, sedangkan akurasi pengujian sebesar 75%. Seluruh kelas pada tahap pelatihan berhasil diklasifikasikan secara tepat, namun pada tahap pengujian terjadi kekeliruan klasifikasi pada beberapa kelas, yang menunjukkan potensi overfitting. Meskipun demikian, hasil ini menunjukkan bahwa citra optic disc dari kamera fundus smartphone memiliki potensi yang baik dalam mendeteksi tingkat keparahan katarak. Penelitian ini menjadi langkah awal menuju sistem deteksi katarak yang efisien, terjangkau, dan mudah diakses, terutama untuk wilayah dengan keterbatasan infrastruktur medis.
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Azam, Bakht, Sami Ur Rahman, Muhammad Irfan, et al. "A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank." Entropy 22, no. 9 (2020): 1040. http://dx.doi.org/10.3390/e22091040.
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Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood—microcytic hyperchromic anemia—which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.
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Virupakshappa, S., Sachinkumar Veerashetty, and N. Ambika. "Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier." Scalable Computing: Practice and Experience 23, no. 4 (2022): 233–49. http://dx.doi.org/10.12694/scpe.v23i4.2024.
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The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.
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Matos-Carvalho, J. P., Filipe Moutinho, Ana Beatriz Salvado, et al. "Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery." Remote Sensing 11, no. 21 (2019): 2501. http://dx.doi.org/10.3390/rs11212501.
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The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV’s mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV’s downwash effect: Static textures- Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures- Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.