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Journal articles on the topic 'Statistical and GLCM'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Sharma, Komal, and Jitendra Virmani. "A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images." International Journal of Ambient Computing and Intelligence 8, no. 2 (2017): 52–69. http://dx.doi.org/10.4018/ijaci.2017040104.

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Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature

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Taha, Mohammed, and Hanaa Ahmed. "Second-Order Statistical Methods GLCM for Authentication Systems." Iraqi Journal for Electrical and Electronic Engineering 17, no. 1 (2021): 1–6. http://dx.doi.org/10.37917/ijeee.17.1.10.

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For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images,

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Karhula, S. S., M. A. J. Finnilä, S. J. O. Rytky, et al. "Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography." Annals of Biomedical Engineering 48, no. 2 (2019): 595–605. http://dx.doi.org/10.1007/s10439-019-02374-2.

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Abstract The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological g

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Suresh, Gulivindala, and Chanamallu Srinivasa Rao. "Copy Move Forgery Detection Using GLCM Based Statistical Features." International Journal on Cybernetics & Informatics 5, no. 4 (2016): 165–71. http://dx.doi.org/10.5121/ijci.2016.5419.

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Eichkitz, Christoph Georg, Marcellus Gregor Schreilechner, Paul de Groot, and Johannes Amtmann. "Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes." Interpretation 3, no. 1 (2015): T13—T23. http://dx.doi.org/10.1190/int-2014-0099.1.

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Texture attributes describe the spatial arrangement of neighboring amplitudes values within a given analysis window. We chose a statistical texture classification method, the gray-level co-occurrence matrix (GLCM), and its derived attributes, to produce a semiautomated description of the spatial arrangement of seismic facies. The GLCM is a measure of how often different combinations of neighboring pixel values occur. We tested the application of directional GLCM-based attributes for the detection of seismic variability within paleoriver features. Calculation of 3D GLCM-based attributes can be

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Sipan, Muhammad, and Rony Kartika Pramuyanti. "ANALISIS TEKSTUR PHOTO LAMA MENGGUNAKAN FITUR TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIKS PADA PEWARNAAN CITRA OTOMATIS." Elektrika 10, no. 1 (2018): 19. http://dx.doi.org/10.26623/elektrika.v10i1.1160.

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<p> Image processing is important in a process of introduction, classification or segmentation or other processes. One thing that can be done is an analysis of the texture features related to old photos in this case grayscale photos. The object of the research can be an old photo (image) and use a statistical method based on Gray Level Counseling Matrix (GLCM). GLCM is one of the methods used for extracting texture features, some of which are analyzed using glcm by comparing the GLCM texture feature in the old photo with the original photo The coloring process is to provide more visualiz

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Alsalihi, Aya, Hadeel K. Aljobouri, and Enam Azez Khalel ALTameemi. "GLCM and CNN Deep Learning Model for Improved MRI Breast Tumors Detection." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 12 (2022): 123–37. http://dx.doi.org/10.3991/ijoe.v18i12.31897.

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Breast cancer is one of the most common types of cancer among Iraqi women. MRI has been used in the detection of breast tumors for its efficient performance in the diagnosis process providing high accuracy. In this paper, breast MRI image data from 89 patients were classified using GLCM and CNN feature extraction methods. Four models were evaluated consisting of GLCM, CNN, combined GLCM and CNN features based models. The statistical ANOVA feature selection method was used to reduce the redundant features. The reduced feature subset was fed to CNN classifier for obtaining either normal or abnor

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Li, Li, Jianfeng Lu, Shanqing Zhang, Linda Mohaisen, and Mahmoud Emam. "Frame Duplication Forgery Detection in Surveillance Video Sequences Using Textural Features." Electronics 12, no. 22 (2023): 4597. http://dx.doi.org/10.3390/electronics12224597.

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Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequenc

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Nugroho, Herminarto, Wahyu Agung Pramudito, and Handoyo Suryo Laksono. "Gray Level Co-Occurrence Matrix (GLCM)-based Feature Extraction for Rice Leaf Diseases Classification." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 4 (2025): 392–400. https://doi.org/10.12928/biste.v6i4.9286.

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In this paper, we propose Gray Level Co-Occurrence Matrix (GLCM) based Feature Extraction to identify and classify rice leaf diseases. An Artificial Neural Network (ANN) algorithm is used to train a classification model. Various statistical features such as energy, contrast, homogeneity, and correlation are extracted from the GLCM matrix to describe the image texture features. After feature removal, an ANN classification model was trained using a dataset consisting of images of healthy and diseased rice leaves. The ANN training process involves optimizing weights and bias using backpropagation

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Neneng, Neneng, Kusworo Adi, and Rizal Isnanto. "Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM)." JURNAL SISTEM INFORMASI BISNIS 6, no. 1 (2016): 1. http://dx.doi.org/10.21456/vol6iss1pp1-10.

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Texture is one of the most important features for image analysis, which provides informations such as the composition of texture on the surface structure, changes of the intensity, or brightness. Gray level co-occurence matrix (GLCM) is a method that can be used for statistical texture analysis. GLCM has proven to be the most powerful texture descriptors used in image analysis. This study uses the four-way GLCM 0o, 45o, 90o, and 135o. Support vector machine (SVM) is a machine learning that can be used for image classification. SVM has a high generalization capability without any requirement of

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Grbatinić, Ivan, and Nebojša T. Milošević. "Incipient UV-Induced Structural Changes in Neutrophil Granulocytes: Morphometric and Texture Analysis of Two-Dimensional Digital Images." Microscopy and Microanalysis 22, no. 2 (2016): 387–93. http://dx.doi.org/10.1017/s1431927616000532.

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AbstractThe aim of this study is to determine the ability and consequent significance of fractal and lacunarity analysis together with computational morphometric and gray-level co-occurrence matrix (GLCM) analysis in detecting subtle initial UVB-induced chromatin and cytosolic changes in neutrophil granulocytes. In addition, the direction and potential significance of the observed changes is speculated. Feulgen-stained neutrophils are pictured and their digitalized images are analyzed in specialized software for digital image processing and ImageJ analysis. Significant statistical difference i

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Yousfi, Laatra, Lotfi Houam, Abdelhani Boukrouche, Eric Lespessailles, Frédéric Ros, and Rachid Jennane. "Texture Analysis and Genetic Algorithms for Osteoporosis Diagnosis." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 05 (2019): 2057002. http://dx.doi.org/10.1142/s0218001420570025.

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Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may

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Faisal, Edi, Agung Nugroho, Ruri Suko Basuki, and Suharnawi Suharnawi. "GLCM Based Locally Feature Extraction On Natural Image." Journal of Applied Intelligent System 7, no. 2 (2022): 128–34. http://dx.doi.org/10.33633/jais.v7i2.6569.

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GLCM is a feature extraction method that uses statistical analysis using a gray scale. Contrast, correlation, energy and entropy are feature features whose value will be sought as the basis for finding the threshold which can then be used to find the threshold value in image segmentation. In this study, a local-based GLCM method is used where the image that has been made into grayscale will be divided into 16 parts of the same size. Each section will look for the value of its GLCM features, namely Contrast, correlation, energy and entropy. The calculation of these four features will be applied

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Kamlesh, Kumar, Ali Wagan Asif, Ahmed Khuhro Mansoor, et al. "Texture based FACE recognition using GLCM and LBP schemes." Indian Journal of Science and Technology 13, no. 13 (2020): 1401–11. https://doi.org/10.17485/IJST/v13i13.118.

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Abstract <strong>Objectives:</strong> Automatic face recognition has been an important area of biometric authentication and verification system in various applications including crime detection, access control, video surveillance, tracking service and other related areas. <strong>Methods/Statistical analysis:</strong> In this study, we present Grey Level Co-occurrence Matrix (GLCM) over Local Binary Patterns (LBP) named as GOL texture feature technique for face classification. The experiments have been conducted on AT & T Cambridge Laboratory face images also called (ORL-fac

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Voigt, Marcelene, Jodie A. Miller, Aubrey N. Mainza, Lunga C. Bam, and Megan Becker. "The Robustness of the Gray Level Co-Occurrence Matrices and X-Ray Computed Tomography Method for the Quantification of 3D Mineral Texture." Minerals 10, no. 4 (2020): 334. http://dx.doi.org/10.3390/min10040334.

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Mineral textural quantification methods have become critical in both geosciences and mineral processing as mineral texture is a critical factor contributing to ore variability. However, the lack of objective mineral texture classification has made quantification difficult. The aim of this study is therefore to investigate the robustness of applying the gray level co-occurrence matrices (GLCM) to 3-dimensional (3D) gray scale images measured by X-ray computed tomography (XCT) for the quantification of mineral texture in 3D. The data quality of the GLCM outputs like statistics, heat maps and his

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Di, Haibin, and Dengliang Gao. "Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation." Interpretation 5, no. 3 (2017): SJ31—SJ40. http://dx.doi.org/10.1190/int-2016-0214.1.

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Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the o

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Chen, Ying, and Feng Yu Yang. "Research on Characteristic Properties of Gray Level Co-Occurrence Matrix." Applied Mechanics and Materials 204-208 (October 2012): 4755–59. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4755.

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Gray level co-occurrence matrix (GLCM) is a second-order statistical measurement. In order to understand the characterization degree of GLCM’s different feature properties, we use images of Brodatz texture images as experimental samples, analyze the change process of feature properties in horizontal, vertical and principal and secondary diagonal directions under the situation of some elements’ dynamic changes such as distance of pixels pair, size of moving window and gray level quantization,. By analyzing the experimental results, this paper can provided certain referential significance in how

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Anika and Navpreet Kaur. "A Review on Heart Disease Detection Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 395. http://dx.doi.org/10.23956/ijarcsse/v7i7/0200.

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The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely

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Rashmi M N, Soumya L M, and Shashikala N. "Image retrieval based on Texture Feature Extraction using GLCM & Wavelets." international journal of engineering technology and management sciences 8, no. 6 (2024): 5–12. https://doi.org/10.46647/ijetms.2024.v08i06.002.

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Retrieving images in an ever increasing and large image datasets has become an exigent task forcomputer vision and pattern recognition applications. In this article, a statistical method thatconsiders the spatial relationship of pixels i.e., the gray-level co-occurrence matrix (GLCM), alsoknown as the gray-level spatial dependence matrix is used to identify texture features. PCA isapplied to reduce the dimensionality of data and build train feature matrix. Five different distancemeasure techniques are used for classification purpose. Proposed method is demonstrated on a verylarge yet challengi

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Pantic, Igor, Svetlana Valjarevic, Jelena Cumic, Ivana Paunkovic, Tatjana Terzic, and Peter R. Corridon. "Gray Level Co-Occurrence Matrix, Fractal and Wavelet Analyses of Discrete Changes in Cell Nuclear Structure following Osmotic Stress: Focus on Machine Learning Methods." Fractal and Fractional 7, no. 3 (2023): 272. http://dx.doi.org/10.3390/fractalfract7030272.

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In this work, we demonstrate that it is possible to create supervised machine-learning models using a support vector machine and random forest algorithms to separate yeast cells exposed to hyperosmotic stress from intact cells. We performed fractal, gray level co-occurrence matrix (GLCM), and discrete wavelet transform analyses on digital micrographs of nuclear regions of interest of a total of 2000 Saccharomyces cerevisiae cells: 1000 exposed to hyperosmotic environments and 1000 control cells. For each nucleus, we calculated values for fractal dimension, angular second moment, inverse differ

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Sahu, Hemant, and Rajeshwari Sahu. "Synthetic Aperture Radar Remote Sensing for Crop Classification." International Journal of Plant & Soil Science 35, no. 12 (2023): 9–16. http://dx.doi.org/10.9734/ijpss/2023/v35i122961.

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This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of

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Guyot, Adrien, Jordan P. Brook, Alain Protat, et al. "Segmentation of polarimetric radar imagery using statistical texture." Atmospheric Measurement Techniques 16, no. 19 (2023): 4571–88. http://dx.doi.org/10.5194/amt-16-4571-2023.

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Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a

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Priambodo, Bagus, Azlina Ahmad, and Rabiah Abdul Kadir. "Spatio-temporal K-NN prediction of traffic state based on statistical features in neighbouring roads." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9059–72. http://dx.doi.org/10.3233/jifs-201493.

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Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic co

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P, Ligisha, and Bhavani S. "Comparison of Fracture Delineation Methods in Anteroposterior Pelvic Radiographs." International Journal of Computer Communication and Informatics 2, no. 2 (2020): 17–29. http://dx.doi.org/10.34256/ijcci2023.

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Pelvic fractures are very difficult to detect due to the visual complexity of the pelvic bone. Pelvic fracture occurs less frequently, only when there is a high energy event such as fall from a height or vehicle collision. In elder people and in osteoporosis patients even a low energy incident may cause fracture. The paper includes the comparison of three different fracture detection methods – GLCM and ANN based, Statistical curve fitting and classifier based and finally statistical curve fitting and ANN based method.

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NURHASANAH, YOULLIA INDRAWATY, IRMA AMELIA DEWI, and FEVLY PALLAR. "Sistem Pengenalan Jenis Kanker Melanoma pada Citra MenggunakanGray Level Co-occurrence Matrices (GLCM) dan K-Nearest Neighbor (KNN) Classifier." MIND Journal 5, no. 1 (2021): 66–80. http://dx.doi.org/10.26760/mindjournal.v5i1.66-80.

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AbstrakMelanoma dikategorikan sebagai bentuk kanker kulit yang paling berbahaya menurut skincancer.org. Kanker kulit ini bertumbuh dan berkembang oleh kerusakan DNA pada sel-sel kulit yang umumnya disebabkan oleh radiasi ultraviolet dari matahari. Pada penelitian ini dibuatkan suatu sistem yang dapat membantu pihak medis untuk memprediksi suatu tipe atau jenis dari suatu kanker melanoma dengan proses antara lain, optimalisasi postprocessing melalui morphological closing, pembentukan matriks-matriks gray level co-occurrence (GLCM) untuk pengekstraksian fitur-fitur tekstur statistika dan K-Neare

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Yuslena, Sari, Budi Prakoso Puguh, and Rizky Baskara Andreyan. "Application of neural network method for road crack detection." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1962–67. https://doi.org/10.12928/TELKOMNIKA.v18i4.14825.

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The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in

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Jadhav, Prof Rupali, Rushiraj Kale, Shubham Kadam, Tushar Gund, and Shreyas Jadhav. "Skin Cancer Detection using Image Processing." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6781–85. http://dx.doi.org/10.22214/ijraset.2023.53282.

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Abstract: Early detection of melanoma, the most dangerous form of skin cancer, is crucial for effective treatment. Melanoma has a higher propensity to metastasize to other parts of the body if not identified and treated promptly. In recent years, non-invasive medical computer vision and medical image processing techniques have emerged as valuable tools in clinical diagnosis for various diseases, including melanoma. These techniques enable automatic image analysis, facilitating accurate and efficient evaluation of skin lesions. The study follows a systematic approach, involving the collection o

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Cha, Sungeun, Joongbin Lim, Kyoungmin Kim, Jongsoo Yim, and Woo-Kyun Lee. "Uncovering the Potential of Multi-Temporally Integrated Satellite Imagery for Accurate Tree Species Classification." Forests 14, no. 4 (2023): 746. http://dx.doi.org/10.3390/f14040746.

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In this study, prior to the launch of compact advanced satellite 500 (CAS500-4), which is an agriculture and forestry satellite, nine major tree species were classified using multi-temporally integrated imageries based on a random forest model using RapidEye and Sentinel-2. Six scenarios were devised considering the composition of the input dataset, and a random forest model was used to evaluate the accuracy of the different input datasets for each scenario. The highest accuracy, with accuracy values of 84.5% (kappa value: 0.825), was achieved by using RapidEye and Sentinel-2 spectral waveleng

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Soeparmi, Soeparmi, Mohtar Yunianto, and Lukmaniyah Rizky Amalia. "Lung Cancer Classification using Gray-Level Co-Occurrence Matrix Feature Extraction and Forward Selection Feature Selection based on the K-Nearest Neighbor Algorithm." INDONESIAN JOURNAL OF APPLIED PHYSICS 15, no. 1 (2025): 133. https://doi.org/10.13057/ijap.v15i1.90378.

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<p class="Abstract">In diagnosing lung cancer, the <em>medical imaging team </em>manually identifies CT-scan<em> images of the lungs. </em>This identification process makes it difficult for the <em>medical imaging team </em>to differentiate between lung cancer and normal images. This is because there is <em>noise </em>in the image, which reduces the image quality, so <em>image processing must reduce the noise</em>. This study used median and Gaussian filters, Otsu thresholding segmentation, GLCM feature extraction, forward selec

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R.Kadhim, Rania, and Mohammed Y. Kamil. "Advancing Dermatological Image Classification: GLCM-Based Machine Learning Insights." Advance Sustainable Science Engineering and Technology 7, no. 1 (2025): 0250116. https://doi.org/10.26877/asset.v7i1.1154.

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The prospects to improve skin illness via the utilization of artificial intelligence algorithms is what renders this study economically important. Machine learning may assist physicians detect people quicker and more accurately. The effective identification of skin disorders using machine learning could result in the development of large and readily available digital tests. A model was used in the present study to analyze the HAM 10000 data. Two hundred images in total were chosen at random; one hundred showed dermatofibroma diseases, whereas the other hundred displayed benign keratosis. Subse

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Sanubary, Iklas. "BRAIN TUMOR DETECTION USING BACKPROPAGATION NEURAL NETWORKS." Indonesian Journal of Physics and Nuclear Applications 3, no. 3 (2018): 83–88. http://dx.doi.org/10.24246/ijpna.v3i3.83-88.

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A study of brain tumor detection has been done by making use of backpropagation neural networks with Gray Level Co-Occurrence Matrix (GLCM) feature extraction. CT-Scan images of the brain consist of 12 normal and 13 abnormal (tumor) brain images are analyzed. The preprocessing stage begins with cropping the image to a 256 x 256 pixels picture, then converting the colored images into grayscale images, and equalizing the histogram to improve the quality of the images. GLCM is used to calculate statistical features determined by 5 parameters i.e., contrast, correlation, energy and homogeneity for

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Savini, Alessandro, Giacomo Feliciani, Michele Amadori, et al. "The Role of Acquisition Angle in Digital Breast Tomosynthesis: A Texture Analysis Study." Applied Sciences 10, no. 17 (2020): 6047. http://dx.doi.org/10.3390/app10176047.

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Background: Digital breast tomosynthesis (DBT) systems employ a sophisticated set of acquisition parameters to generate an image set, and the DBT acquisition angle is considered to be one of the most important parameters. The aim of this study was to use texture analysis to assess how the DBT acquisition angle might influence DBT images of breast parenchyma. Methods: Thirty-four patients were selected from a clinical study conducted at IRST Institute. Each patient underwent a dual DBT scan performed with Fujifilm Amulet Innovality (Fujifilm Corp, Tokyo, Japan) in standard (ST, angular range =

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Ossai, Chinedu I., and Nilmini Wickramasinghe. "GLCM and statistical features extraction technique with Extra-Tree Classifier in Macular Oedema risk diagnosis." Biomedical Signal Processing and Control 73 (March 2022): 103471. http://dx.doi.org/10.1016/j.bspc.2021.103471.

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Alzubaydi, Dhia, and Shyma Akram. "Probabilistic Neural Network with GLCM and Statistical Measurements for Increasing Accuracy of Iris Recognition System." International Journal of Computer Applications 136, no. 12 (2016): 44–51. http://dx.doi.org/10.5120/ijca2016908628.

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A, Selin Vironicka, and Sathiaseelan J.G.R. "TEXTURE FEATURE EXTRACTION WITH MEDICAL IMAGE USING GLCM AND MACHINE LEARNING TECHNIQUES." ICTACT Journal on Image and Video Processing 12, no. 4 (2022): 2735–40. https://doi.org/10.21917/ijivp.2022.0388.

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Bones are a vital component of the human body. Bone provides the capacity to move the body. Humans have a high rate of bone fractures. The X-ray image is used by the doctors to identify the fractured bone. The manual fracture identification technique takes a long time and has a high risk of mistake. Machine learning and artificial intelligence are critical in resolving difficult difficulties in clinical imaging. Both medical practitioners and patients benefit from machine learning and artificial intelligence. Nowadays, an automatic system is built to detect abnormalities in bone X-ray pictures

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Guan, HaiXiang, HuanJun Liu, XiangTian Meng, et al. "A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages." Remote Sensing 12, no. 19 (2020): 3149. http://dx.doi.org/10.3390/rs12193149.

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Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compa

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Kim, Tae-Yun, Nam-Hoon Cho, Goo-Bo Jeong, Ewert Bengtsson, and Heung-Kook Choi. "3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading." Computational and Mathematical Methods in Medicine 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/536217.

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One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D g

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Najjar, Fallah H., Karrar A. Kadhim, Munaf Hamza Kareem, Hanan Abbas Salman, Duha Amer Mahdi, and Horya M. Al-Hindawi. "Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning." Malaysian Journal of Fundamental and Applied Sciences 19, no. 3 (2023): 389–98. http://dx.doi.org/10.11113/mjfas.v19n3.2911.

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As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical tex

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Kaesmetan, Yampi R. "PERBANDINGAN EKSTRAKSI TEKSTUR CITRA UNTUK PEMILIHAN BENIH KEDELAI DENGAN METODE STATISTIK ORDE I DAN STATISTIK ORDE II." High Education of Organization Archive Quality: Jurnal Teknologi Informasi 10, no. 2 (2018): 92–102. http://dx.doi.org/10.52972/hoaq.vol10no2.p92-102.

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The problem in determining the selection of soybean seeds for replanting, especially in East Nusa Tenggara is still an important issue. The thing that affects the quality of soybean seeds is found broken seeds, dull seeds, dirty seeds, and broken seeds due to the process of drying and shelling. Determination of soy bean quality is usually done manually by visual observation. The manual system takes a long time and produces products with inconsistent quality due to visual limitations, fatigue, and different perceptions of each observer. This research was conducted using comparison of image text

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R. Saleh, Shaymaa, Zamen F. Jabr, and Abeer N. Fasial. "A Hybrid Features for Signature Recognition Using Neural Network." University of Thi-Qar Journal of Science 6, no. 1 (2016): 83–89. http://dx.doi.org/10.32792/utq/utjsci/v6i1.52.

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In automatic personal recognition systems, biometric features is used as recognition measure based on biological traits such as face, iris, fingerprint, etc…or gait, signature which is considered behavioral characteristics. Signature verification is one of the authentication methods which can provide security at maintenance and low cost. The most essential and challenging stage of any off-line signature system is feature extraction stage. The accuracy and robust of the recognition system depends basically on the usefulness of the signature features extracted by this system. If the extracted fe

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Zakaria, Alaa Khaled, Yasser Khadra, and Eid Al-Abboud. "Selection of the optimum features to identify tooth decay in the panoramic images based on image texture analysis." Association of Arab Universities Journal of Engineering Sciences 27, no. 1 (2020): 130–39. http://dx.doi.org/10.33261/jaaru.2019.27.1.014.

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    The process of identifying pathological patterns in dental radiographic images (panorama images) is one of the most important stages of diagnosing diseases for dentists, and in light of the tremendous technological development, especially in the field of machine learning and pattern recognition, the Digital Image processing department has the most important role in the field of image fragmentation Extract the necessary features in order to identify pathological patterns and thus easily extract the pathological features of the input images. In this research, a method

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Bhattacharjee, Subrata, Cho-Hee Kim, Hyeon-Gyun Park, et al. "Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features." Cancers 11, no. 12 (2019): 1937. http://dx.doi.org/10.3390/cancers11121937.

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Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occu

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Priya, Sunkara Santhi, and Dr Deepti Sharma. "A Deep Learning Approach Combining GLCM Features with ‎UNet++ and GNN for Salt Body Detection in Seismic Data." International Journal of Basic and Applied Sciences 14, no. 3 (2025): 94–100. https://doi.org/10.14419/q28hjj73.

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Accurate detection and segmentation of salt bodies in seismic data is critical for effective subsurface interpretation and hydrocarbon exploration. Traditional methods often struggle with complex geological patterns, motivating the use of deep learning for enhanced accuracy. In this ‎study, we propose a hybrid deep learning framework that integrates Gray Level Co-occurrence Matrix (GLCM) texture features with the ‎UNet++ architecture and Graph Neural Networks (GNNs) to improve salt body detection. The model combines handcrafted texture ‎descriptors with deep hierarchical features and spatial r

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Fauzi, Arthur Ahmad, Fitri Utaminingrum, and Fatwa Ramdani. "Road surface classification based on LBP and GLCM features using kNN classifier." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1446–53. http://dx.doi.org/10.11591/eei.v9i4.2348.

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Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One c

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Arthur, Ahmad Fauzi, Utaminingrum Fitri, and Ramdani Fatwa. "Road surface classification based on LBP and GLCM features using kNN classifier." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1446–53. https://doi.org/10.11591/eei.v9i4.2348.

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Autonomous ground vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these processes require a fast-computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One

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Kilbride, John B., and Robert E. Kennedy. "A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery." Remote Sensing 16, no. 23 (2024): 4586. https://doi.org/10.3390/rs16234586.

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Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha−1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from Gray Level Co-occurrence Matrix (GLCM) statistics, many spatial feature engineering techniques (e.g., morphological operations or edge detectors) have not been evaluated in the context of forest AGB estimation. Moreover, many prior investigations have been constrained by limited geographic domains and sample s

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Latifah, Khoiriya, Abdul Rochim, and Bambang Supriyadi. "IDENTIFIKASI SERAT BAMBU MENGGUNAKAN EKSTRAKSI CIRI STATISTIK ORDE 2 (GLCM) DAN PENGUKURAN JARAK K-NN." JURNAL TEKNIK INFORMATIKA 12, no. 2 (2019): 177–82. http://dx.doi.org/10.15408/jti.v12i2.8946.

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Indonesia is a large bamboo producer. Many benefits can be taken from bamboo trees, among others, as an alternative material for environmentally friendly construction, handicraft, and even become a safe material for use. Based on the property of its mechanical strength, bamboo has high tensile strength and fiber content, including fiber length, inter-fiber adhesive, namely lignin and the higher diameter of bamboo fiber, causing bamboo stems to become stronger and stiffer so that bamboo quality is getting better. One objective is to use a texture analysis of statistical features extraction of d

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Tabbakh, Amer, and Soubhagya Sankar Barpanda. "Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features." Traitement du Signal 39, no. 6 (2022): 1893–905. http://dx.doi.org/10.18280/ts.390602.

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Saifullah, Shoffan. "K-MEANS CLUSTERING FOR EGG EMBRYO'S DETECTION BASED-ON STATISTICAL FEATURE EXTRACTION APPROACH OF CANDLING EGGS IMAGE." SINERGI 25, no. 1 (2020): 43. http://dx.doi.org/10.22441/sinergi.2021.1.006.

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This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The sta

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Karthick, P. A., M. Navaneethakrishna, N. Punitha, A. R. Jac Fredo, and S. Ramakrishnan. "Analysis of muscle fatigue conditions using time-frequency images and GLCM features." Current Directions in Biomedical Engineering 2, no. 1 (2016): 483–87. http://dx.doi.org/10.1515/cdbme-2016-0107.

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AbstractIn this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the time-frequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT). Gray-Level Co-occurrence Matrix (GLCM) is extracted from low

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