Relevant bibliographies by topics / GLRLM (gray level run length matrix)

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Academic literature on the topic 'GLRLM (gray level run length matrix)'

Author: Grafiati
Published: 2 June 2025
Last updated: 5 July 2025

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Journal articles on the topic "GLRLM (gray level run length matrix)"

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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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Book chapters on the topic "GLRLM (gray level run length matrix)"

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Mushtaq, Saba, and Ajaz Hussain Mir. "Copy-Move Detection Using Gray Level Run Length Matrix Features." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7395-3_46.

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Danilov, Gleb, Elizaveta Makashova, Mikhail Galkin, and Kristina Karandasheva. "Radiogenomics in NF2-Associated Schwannomatosis (Neurofibromatosis Type II): Exploratory Data Analysis." In Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230565.

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Our pilot study aimed at exploratory radiogenomic data analysis in patients with NF2-associated schwannomatosis (formerly neurofibromatosis type II) to assume the potential of image biomarkers in this pathology. Fifty-three unrelated patients (37 (69.8%) women, avg. age 30.2 ± 11.2 y.o.) were enrolled in the study. First-order, gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and geometry-based statistics were calculated (3718 features per region of interest). We demonstrated imaging patterns and statistically significant differences in radiomic features potentially related to the genotype and clinical phenotype of the disease. However, the clinical utility of these patterns should be further evaluated. The study was supported by the Russian Science Foundation grant 21-15-00262.
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Kaur, Parminder, Prabhpreet Kaur, and Gurvinder Singh. "Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch015.

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Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. Based upon the analysis of existing algorithms for the automatic fetal development measurement, a new algorithm known as neuro-fuzzy based on genetic algorithm is developed. Firstly, the fetal ultrasound benchmark image is auto-pre-processed using normal shrink homomorphic technique. Secondly, the features are extracted using gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM), intensity histogram (IH), and rotation invariant moments (IM). Thirdly, neuro-fuzzy using genetic approach is used to distinguish among the fetus growth as abnormal or normal. Experimental results using benchmark and live dataset demonstrate that the developed method achieves an accuracy of 97% as compared to the state-of-the-art methods in terms of parameters such as sensitivity, specificity, recall, f-measure, and precision rate.
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Gupta, Praveen, Nagendra Kumar, Ajad, N. Arulkumar, and Muthukumar Subramanian. "Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging." In AI and IoT-based Intelligent Health Care & Sanitation. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815136531123010013.

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Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR – True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm.
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García G., Maiora J., Tapia A., and De Blas M. "Computer-aided Diagnosis of Abdominal Aortic Aneurysm after Endovascular Repair Using Texture Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-105-2-716.

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Endovascular repair is a minimal invasive alternative to open surgical therapy. From a long term perspective, complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear influencing the evolution of treatment. The objective of this work is to develop a preliminary Computer-aided diagnosis system (CAD) for an automated classification of EVAR progression from computed tomography angiography CTA images. The system is based on the extraction of texture features from thrombus aneurysm samples and a posterior classification. Regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three conventional texture-analysis methods such as the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), and the gray level difference method (GLDM), were applied to each ROI to obtain texture features. Classification of the ROI is carried out by three different strategies. In the first one each feature set is fed to a neural network (NN). The second consists of a single neural network fed with a reduced version of texture features after a feature selection process. The third one comprised an ensembles of classifiers (ECs), formed by three NNs, each using as input one of the feature sets. The final decision is based on the application of a voting scheme across the outputs of the individual NNs. Classification results from the three classification strategies are evaluated using a receiver operating-characteristics (ROC) analysis and area under the roc curve (Az) performance. The multiple classification scheme using the three sets of texture features results in a better performance, as compared to the classification performance of the other alternatives.
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Conference papers on the topic "GLRLM (gray level run length matrix)"

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Raju, P. Purna Chandra, Bhuvaneswari Balachander, and S. Neeharika. "Expression of Concern for: Comparison of Haralick Texture Features and Gray Level Run Length Matrix Features for Analyzing Textural Variation in Cotton Leaves to Identify Spot Disease." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703555.

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Ghaemmaghami, Shahrokh, and Seyed Mojtaba Seyedhosseini Seyedhosseini Tarzjani. "Detection of LSB Replacement and LSB Matching Steganography Using Gray Level Run Length Matrix." In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2009. IEEE, 2009. http://dx.doi.org/10.1109/iih-msp.2009.68.

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Mentari, Mustika, Cahya Rahmad, Moch Syifa’ Muchlisin, and Septian Enggar Sukmana. "Classification of Siam Orange Ripeness Level using K-Nearest Neighbors Algorithm and Features Gray Level Run Length Matrix." In 2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2023. http://dx.doi.org/10.1109/comnetsat59769.2023.10420620.

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Simarmata, Togi SiholMarito, R. Rizal Isnanto, and Aris Triwiyatno. "Detection of Pulmonary Tuberculosis Using Neural Network with Feature Extraction of Gray Level Run-Length Matrix Method on Lung X-Ray Images." In 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2023. http://dx.doi.org/10.1109/isitia59021.2023.10221153.

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Raju, P. Purna Chandra, Bhuvaneswari Balachander, and S. Neeharika. "Comparison of Haralick Texture Features and Gray Level Run Length Matrix Features for Analyzing Textural Variation in Cotton Leaves to Identify Spot Disease." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10023043.

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