Journal articles: 'Grey Level Co-Occurrence Matrix (GLCM)'

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Toshikj, Emilija, and Bojan Prangoski. "Grey level co-occurrence matrix (GLCM) for textile print analysis." Tekstilna industrija 70, no. 4 (2022): 34–40. http://dx.doi.org/10.5937/tekstind2204034t.

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Print mottle is a print defect. This print defect has great attention in print quality assessment. Print mottle is determined by the grey level co-occurrence matrix (GLCM). An important parameter in the GLCM processing is the direction angle of pixels in the digitalized print image. This research aimed to investigate the influence of the direction angle, which is an important input parameter in GLCM processing, on the output parameters, such as entropy, energy, contrast, correlation, and homogeneity. Hence, prints were generated in four different colors (cyan, magenta, yellow and black) on white polyester elastase fabric by sublimation printing. The non-uniformity of the print for each color was processed at different direction angles, such as 0° (horizontal), 90° (vertical), 45° (right-diagonal), and 135° (left-diagonal). Values for GLCM parameters obtained at different direction angles were slightly different regardless of print color. The choice of direction angle influenced the values of GLCM parameters. The average of all four directional angle values obtained for each GLCM parameter was taken. The GLCM processing method can be used for prints of different colors, patterns, and different quality levels to evaluate their print uniformity.

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Datta, Anurup, Samik Dutta, Surjya K. Pal, Ranjan Sen, and Sudipta Mukhopadhyay. "Texture Analysis of Turned Surface Images Using Grey Level Co-Occurrence Technique." Advanced Materials Research 365 (October 2011): 38–43. http://dx.doi.org/10.4028/www.scientific.net/amr.365.38.

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The main purpose of this work was to study the applicability of an image texture analysis method, namely, the grey level co-occurrence matrix (GLCM) method for the examination of the smoothness of the images of a turned surface. The effect of the variation of the pixel pair spacing (pps) on the construction of the GLCM was also considered and then, contrast and homogeneity were calculated from the GLCMs which served as texture descriptors for the quality of the machined surface. Finally, the variation of these texture descriptors with cutting time was analyzed and compared with the variation of tool wear and surface roughness with cutting time.

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WALKER, ROSS F., PAUL T. JACKWAY, and DENNIS LONGSTAFF. "GENETIC ALGORITHM OPTIMIZATION OF ADAPTIVE MULTI-SCALE GLCM FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 01 (2003): 17–39. http://dx.doi.org/10.1142/s0218001403002228.

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We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification "worth" is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

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Rahman, Muhammad Saidi. "KLASIFIKASI MOTIF SASIRANGAN BERBASIS FITUR GREY LEVEL CO-OCCURRENCE MATRIES MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK." Technologia: Jurnal Ilmiah 9, no. 4 (2018): 250. http://dx.doi.org/10.31602/tji.v9i4.1540.

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Sasirangan adalah kain khas Kalimantan selatan yang dibuat dengan teknik jerujuk. Sasirangan memiliki banyak motif yang berjumlah sekitar 20 motif. Pada penelitian ini motif yang digunakan untuk klasifikasi sasirangan ada 3 motif yaitu Abstrak, Kulat Kurikit dan Hiris Gegatas dengan jumlah citra yang digunakan pada penitilian ini setiap motif ada 10 citra. Grey Level Co-occurrence Matrix (GLCM) digunakan untuk ekstrasi fitur pada gambar sasirangan. Dengan mengambil nilai 5 besaran dari GLCM yaitu Entropi, Korelasi, Kontras, Angular Second Moment (ASM) dan Inverse Different Moment (IDM) dari 4 sudut citra yang berbeda yaitu 00, 450, 900 dan 1350. Selanjutnya hasil ekstrasi akan di klasifikasikan dengan menggunakan metode Backpropagation Neural Network dengan beberapa skenario pengujian melalui X-Validation. Tipe validasi yang diuji yaitu Stratified Sampling, Linear Sampling dan Shuffled Sampling dengan ketentuan Number Validation 2 sampai 10. Hasil akurasi tertinggi pada number validation 10 dengan akurasi 95% pada ketiga tipa validasi. Keywords : Sasirangan, Grey Level Co-occurrence Matrix(GLCM), Backpropagation Neural Network.

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Nguyen, Hoang Duc, Thuong Tien Le, Tuan Hong Do, and Cao Thu Bui. "A NEW DESCRIPTOR FOR IMAGE RETRIEVAL USING CONTOURLET COOCCURRENCE." Science and Technology Development Journal 15, no. 2 (2012): 5–16. http://dx.doi.org/10.32508/stdj.v15i2.1785.

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In this paper, a new descriptor for the feature extraction of images in the image database is presented. The new descriptor called Contourlet Co-Occurrence is based on a combination of contourlet transform and Grey Level Co-occurrence Matrix (GLCM). In order to evaluate the proposed descriptor, we perform the comparative analysis of existing methods such as Contourlet [2], GLCM [14] descriptors with Contourlet Co-Occurrence descriptor for image retrieval. Experimental results demonstrate that the proposed method shows a slight improvement in the retrieval effectiveness.

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Naibaho, Rizky Fauzan, and Indah Purnama Sari. "Implementasi Metode Gray Level Co-Occurrence Matrix Menganalisis Tekstur Kulit Wajah." sudo Jurnal Teknik Informatika 3, no. 4 (2025): 172–82. https://doi.org/10.56211/sudo.v3i4.668.

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GLCM merupakan metode analisis gambar yang dapat mengukur berbagai fitur tekstur seperti kontras, korelasi, energi, dan homogenitas dari gambar bergradasi abu-abu. Skripsi ini bertujuan untuk mengeksplorasi efektivitas GLCM dalam mengekstraksi dan menganalisis informasi tekstur dari kulit wajah, serta aplikasinya dalam bidang seperti deteksi penyakit kulit dan pengenalan wajah. dalam penelitian ini, gambar kulit wajah dikumpulkan dan diproses menggunakan teknik GLCM untuk menghasilkan matriks co-occurrence pada berbagai jarak dan sudut. Fitur-fitur tekstur yang dihasilkan dari GLCM kemudian dianalisis untuk menentukan pola-pola tekstur khas yang dapat dikaitkan dengan kondisi kulit atau identitas individu. Hasil penelitian menunjukkan bahwa GLCM dapat memberikan informasi tekstur yang signifikan dan relevan dalam konteks analisis kulit wajah, dengan potensi aplikasi dalam teknologi kesehatan dan keamanan.

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Singh, Amarpreet, and Sanjogdeep Singh. "Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries." Journal of Image and Graphics 11, no. 1 (2023): 82–90. http://dx.doi.org/10.18178/joig.11.1.82-90.

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With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

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Dhanalakshmi, P., and Dr G. Satyavathy. "Grey Level Co-Occurrence Matrix (GLCM) and Multi-Scale Non-Negative Sparse Coding For Classification of Medical Images." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 481–93. http://dx.doi.org/10.5373/jardcs/v11sp10/20192835.

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Chen, Chaoyue, Hongyu Zhuo, Xiawei Wei, and Xuelei Ma. "Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy." Contrast Media & Molecular Imaging 2019 (November 12, 2019): 1–7. http://dx.doi.org/10.1155/2019/5481491.

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Introduction. The purpose of this study was to evaluate the prognostic value of texture features on contrast-enhanced magnetic resonance imaging (MRI) for patients with primary central nervous system lymphoma (PCNSL). Methods. In this retrospective study, fifty-two patients diagnosed with PCNSL were enrolled from October 2010 to March 2017. The texture feature of tumor tissue on the histogram-based matrix (histo-) and the grey-level co-occurrence matrix (GLCM) was retrieved by contrast-enhanced T1-weighted imaging before any antitumor treatment. Receiver operating characteristic curve analyses were performed to obtain their optimal cutoff values, based on which we dichotomized patients into subgroups. The Kaplan–Meier analyses were conducted to compare overall survival (OS) of subgroups, and multivariate Cox regression analyses were used to determine if they could be taken as independent prognostic factors. Results. Ten texture features were extracted from the MR image, including Energy, Entropy, Kurtosis, Skewness on the histogram-based matrix, and Correlation, Contrast, Dissimilarity, Energy, Entropy, and Homogeneity on the grey-level co-occurrence matrix. Three of them (GLCM-Contrast, GLCM-Dissimilarity, and GLCM-Homogeneity) are shown to be significant in relation to overall survival (OS). The multivariate Cox regression analyses suggest that GLCM-Homogeneity could be taken as independent predictors. Conclusions. The texture features of contrast-enhanced magnetic resonance imaging (MRI) could potentially serve as prognostic biomarkers for PCNSL patients.

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Wang, Hui, Xiang Wei Kong, and Han Wang. "Research on Building Method of Gray Level Co-Occurrence Matrix Suitable to Natural Texture." Advanced Materials Research 1079-1080 (December 2014): 432–35. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.432.

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CIn order to build gray level co-occurrence matrix suitable to natural texture, a method based on separable criterion was proposed. Combined correlation matrix of feature parameters with character of natural texture, 5 independent feature parameters are extracted from 11 feature parameters of gray level co-occurrence matrix (GLCM). Building factors of GLCM, which is appropriate to describe wood texture, are confirmed by using the separable criterion, when d equals to 2 and g equals to 16.

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Ilmawan, Fachrul, and Agung Ramadhanu. "IMPLEMENTASI METODE K-MEANS UNTUK KLASTERISASI VARIETAS PARPIKA DENGAN MENGGUNAKAN TEKNIK PENGOLAHAN CITRA DIGITAL." Jurnal Informatika Teknologi dan Sains (Jinteks) 7, no. 1 (2025): 249–54. https://doi.org/10.51401/jinteks.v7i1.5426.

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Dengan menggunakan kombinasi segmentasi objek, ekstraksi bentuk, dan ekstraksi tekstur, penelitian ini bertujuan untuk melakukan klasterisasi pada varietas paprika melalui penggunaan K-Means Clustering dan Gray-Level Co-Occurrence Matrix (GLCM). Segmentasi objek dilakukan menggunakan algoritma K-Means Clustering untuk membedakan objek dari latar belakangnya. Selanjutnya, proses ekstraksi tekstur dan bentuk dilakukan menggunakan Matriks Co-Occurrence Level Gray (GLCM) untuk membedakan jenis varietas paprika. Hasil kalsterisasi dicapai melalui penggunaan aplikasi matlab, yang mencakup import data, konversi RBG ke L*a*b, segmentasi objek dengan latar belakang menggunakan K-Means Clustering, dan kemudian menggunakan metode Gray-Level Co-Occurrence Matrix (GLCM) untuk ekstraksi bentuk dan tekstur. Hasil penelitian tentang klasterisasi varietas paprika juga menunjukkan bahwa proses itu berhasil. Model berhasil mengidentifikasi setiap sampel gambar dengan akurat seratus persen dengan menggunakan sampel delapan gambar paprika merah dan hijau. Penggunaan algoritma clusteriang K-means dan Matriks Co-Occurrence Level Gray (GLCM) menunjukkan hasil yang sangat baik; ini membuktikan efektivitasnya dalam melakukan klasterisasi pada varietas paprika.

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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 to select feature properties reasonable in the application of image retrieval and classification and identification which are based on using GLCM as feature.

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García, G., J. Maiora, M.A. Tapia, and Blas M. De. "EVALUATION OF TEXTURE FOR CLASSIFICATION OF ABDOMINAL AORTIC ANEURYSM AFTER ENDOVASCULAR REPAIR." JOURNAL OF DIGITAL IMAGING 25, no. 3 (2012): 369–76. https://doi.org/10.1007/s10278-011-9417-7.

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The use of the endovascular prostheses in abdominal aortic aneurysm has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long-term perspective, complications such as leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present, computed tomographic angiography (CTA) is the most common examination for medical surveillance. However, endoleak complications cannot always be detected by visual inspection on CTA scans. The investigation on new techniques to detect endoleaks and analyse their effects on treatment evolution is of great importance for endovascular aneurysm repair (EVAR) technique. The purpose of this work was to evaluate the capability of texture features obtained from the aneurysmatic thrombus CT images to discriminate different types of evolutions caused by endoleaks. The regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three techniques were applied to each ROI to obtain texture parameters, namely the grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRLM) and the grey level difference method (GLDM). The results showed that GLCM, GLRLM and GLDM features presented a good discrimination ability to differentiate between favourable or unfavourable evolutions. GLCM was the most efficient in terms of classification accuracy (93.41% ± 0.024) followed by GLRLM (90.17% ± 0.077) and finally by GLDM (81.98% ± 0.045). According to the results, we can consider texture analysis as complementary information to classified abdominal aneurysm evolution after EVAR.

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Tang, Wenhao. "Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis." EAI Endorsed Transactions on e-Learning 8, no. 2 (2023): e4. http://dx.doi.org/10.4108/eetel.v8i2.3091.

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As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images, manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extract image features and classify images, respectively. The experimental results of my proposed GLCM-RVFL model are validated using K-fold cross-validation, and the indicators are 78.81±1.75%, 77.08±0.68%, 77.46±0.73%, 54.22±1.35%, and 77.48±0.74% for sensitivity, accuracy, F1-score, MCC, and FMI, respectively, which also confirms that the proposed model performs well on the COVID-19 detection task. After comparing with six state-of-the-art COVID-19 detection, I ensured that my model achieved higher performance.

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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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Gupta, Chaahat, Naveen Kumar Gondhi, and Parveen Kumar Lehana. "Gray Level Co-Occurrence Matrix (GLCM) Parameters Analysis for Pyoderma Image Variants." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 353–58. http://dx.doi.org/10.1166/jctn.2020.8674.

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Analysis of different visual textures present in the given images is one of the important perspectives of human vision for objects segregation and identification. Texture-based features are widely used in medical diagnosis for informal prediction of dermatological diseases. Dermatological diseases are the most universal diseases affecting all the living beings worldwide. Recent advancements in image processing have considerably improved the classification, identification, and treatment of various dermatological diseases. Present paper reports the results of Gray Level Co-occurrence Matrix (GLCM) based texture analysis of skin diseases for parametric variations. The investigations were carried out using three Pyoderma variants (Boil, Carbuncle, and Impetigo Contagiosa) using GLCM. GLCM parameters (Energy, Correlation, Contrast, and Homogeneity) were extracted for each colour component of the images taken for the investigation. Contrast, correlation, energy, and homogeneity represent the coarseness, linear dependency, textural uniformity, and pixel distribution of the texture, respectively. The analysis of the GLCM parameters and their histograms showed that the said textural features are disease dependent. The approach may be used for the identification of dermatological diseases with satisfactory accuracy by employing a suitable machine learning algorithm.

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Suharjito, Imran Bahtiar, and Girsang Suganda. "Family Relationship Identification by Using Extract Feature of Gray Level Co-occurrence Matrix (GLCM) Based on Parents and Children Fingerprint." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2738–45. https://doi.org/10.11591/ijece.v7i5.pp2738-2745.

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This study aims to find out the relations correspondence by using Gray Level Co-occurrence Matrix (GLCM) feature on parents and children finger print. The analysis is conducted by using the finger print of parents and family in one family There are 30 families used as sample with 3 finger print consists of mothers, fathers, and children finger print. Fingerprints data were taken by fingerprint digital persona u are u 4500 SDK. Data analysis is conducted by finding the correlation value between parents and children fingerprint by using correlation coefficient that gained from extract feature GLCM, both for similar family and different family. The study shows that the use of GLCM Extract Feature, normality data, and Correlation Coefficient could identify the correspondence relations between parents and children fingerprint on similar and different family. GLCM with four features (correlation, homogeneity, energy and contrast) are used to give good result. The four sides (0 o , 45 o , 90 o and 135 o ) are used. It shows that side 0 o gives the higher accurate identification compared to other sides.

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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 done in 13 space directions. The results of GLCM-based attribute calculation differed depending on the chosen GLCM parameters (number of gray levels, analysis window, and direction of calculation). We specifically focused on how the direction of calculation influenced the computation of attributes, while keeping other parameters constant. We first tested the workflow on a 2D training image and later ran on a real seismic amplitude volume from the Vienna Basin. Based on the GLCM-based attributes, we could map the channel features and extract them as geobodies. Additionally, we generated a new set of directional GLCM-based attributes to detect spatial changes in the seismic facies. By comparing these directional attributes, we could determine areas within the channel features having higher directional variability. Areas with higher tendency to directional variations might be associated with changes in lithology, seismic facies, or with seismic anisotropy.

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Widyaningsih, Maura. "Identifikasi Kematangan Buah Apel Dengan Gray Level Co-Occurrence Matrix (GLCM)." Jurnal SAINTEKOM 6, no. 1 (2017): 71. http://dx.doi.org/10.33020/saintekom.v6i1.7.

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Digital image processing is part of the technological developments in the concepts and reasoning, the human wants the machine (computer) can recognize images like human vision. Recognizing the image is one way to distinguish the traits that exist in the image. Texture is one of the characteristics that distinguish the image, is the basic characteristic of the image identification. Gray Level Co-Occurrence Matrix (GLCM) is one method of obtaining characteristic texture image by calculating the probability of adjacency relationship between two pixels at a certain distance and direction. The characteristics of texture obtained from GLCM methods include contrast, correlation, homogeneity, and energy. The extracted features are then used for identification with the nearest distance calculations (Eucledian Distance). The final results analysis program to identify the category of apples raw, half-ripe or overripe. Training data used are 12 images apple, consisting of 4 is crude, 4 is half-cooked, and 4 is ripe, 7 data used for testing. Testing GLCM with 00 angle feature extraction results of the test images can be recognized by a factor Eucledian Distance to the query image. Identification of test data is information all the data can be recognized. Eucledian Distance is a method that helps the introduction of a test object data.

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Karnati, Srivathsav, and Biswajit Pathak. "Analysis of biospeckle pattern using grey-level and color-channel assessment methods." Laser Physics 34, no. 10 (2024): 105601. http://dx.doi.org/10.1088/1555-6611/ad7720.

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Abstract Biospeckle offers a practical tool for contact-free testing and monitoring of biological samples, providing unique insights into dynamics of biological processes. In the present work, we design an experimental arrangement to perform quality assessment on biological samples using biospeckle patterns. We analyse the speckle patterns and evaluate its important parameters by constructing a grey-level co-occurrence matrix (GLCM). Furthermore, we propose an alternative and reliable method to study the biospeckle patterns by constructing a color-channel assessment matrix. The proposed approach provides both qualitative and quantitative information of the sample under study, with minimum speckle images and no stringent requirement of correct parameter selection, unlike in the case of GLCM method. Proof-of-concept experimental results are provided that demonstrate the feasibility of the proposed method in evaluating the quality of biological samples.

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Pantic, Igor, Sanja Dacic, Predrag Brkic, et al. "Application of Fractal and Grey Level Co-Occurrence Matrix Analysis in Evaluation of Brain Corpus Callosum and Cingulum Architecture." Microscopy and Microanalysis 20, no. 5 (2014): 1373–81. http://dx.doi.org/10.1017/s1431927614012811.

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AbstractThis aim of this study was to assess the discriminatory value of fractal and grey level co-occurrence matrix (GLCM) analysis methods in standard microscopy analysis of two histologically similar brain white mass regions that have different nerve fiber orientation. A total of 160 digital micrographs of thionine-stained rat brain white mass were acquired using a Pro-MicroScan DEM-200 instrument. Eighty micrographs from the anterior corpus callosum and eighty from the anterior cingulum areas of the brain were analyzed. The micrographs were evaluated using the National Institutes of Health ImageJ software and its plugins. For each micrograph, seven parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, GLCM variance, fractal dimension, and lacunarity. Using the Receiver operating characteristic analysis, the highest discriminatory value was determined for inverse difference moment (IDM) (area under the receiver operating characteristic (ROC) curve equaled 0.925, and for the criterion IDM≤0.610 the sensitivity and specificity were 82.5 and 87.5%, respectively). Most of the other parameters also showed good sensitivity and specificity. The results indicate that GLCM and fractal analysis methods, when applied together in brain histology analysis, are highly capable of discriminating white mass structures that have different axonal orientation.

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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 original reflection patterns could be best preserved. However, the seismic features of interpretational interest often cover only a small portion of its amplitude histogram. For representing such features more effectively, it is helpful to perform a nonlinear rescaling of the amplitude distribution between different seismic features. To achieve such an objective, this study proposes a nonlinear GLCM analysis based on four types of nonlinear gray-level transformation (logarithmic, exponential, sigmoid, and logit) and investigates their implications for seismic facies interpretation. Applications to the 3D seismic data set from offshore Angola (West Africa) demonstrate the added values of the generated nonlinear GLCM attributes in better characterizing the channels, fans, and lobes in a deep-marine turbidite system.

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Eichkitz, Christoph Georg, Johannes Amtmann, and Marcellus Gregor Schreilechner. "Computation of grey level co-occurrence matrix (GLCM) attributes in single and multiple directions." First Break 38, no. 3 (2020): 57–62. http://dx.doi.org/10.3997/1365-2397.fb2020019.

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Ou, Xiang, Wei Pan, and Perry Xiao. "In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM)." International Journal of Pharmaceutics 460, no. 1-2 (2014): 28–32. http://dx.doi.org/10.1016/j.ijpharm.2013.10.024.

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Mahrus ali, Muhammad Mahrus. "DETEKSI JALAN BERLUBANG MENGGUNAKAN METODE GREY LEVEL CO-OCCURRENCE MATRIX DAN NEURAL NETWORK." COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi 3, no. 1 (2022): 01–08. http://dx.doi.org/10.33650/coreai.v3i1.4088.

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Faktor utama yang menjadi penentu kelayakan kondisi suatu jalan yaitu kerusakan pada aspal sehingga pemeliharaan jalan perlu dilakukan secara berkala. Pemeriksaan kondisi jalan dilakukan oleh petugas survey dengan melakukan pengamatan langsung pada jalan yang akan diberikan penilaian secara manual. Aktifitas pemeriksaan dapat mengganggu kelancaran arus lalu lintas pada jalan yang padat kendaraan terlebih lagi dapat membahayakan keselamatan petugas survey. Diperlukan alternative pemeriksaan jalan untuk menghindari ancaman yang tidak diinginkan dan dapat membiat biaya lebih efektif. Pada penelitian ini dikembangkan suatu system deteksi jalan berlubang menggunakan data video, dengan menerapkan metode Gray Level Co-occurrence Matrix (GLCM) dan klasifikasi menggunakan Neural Network. Deteksi terdiri atas 2 tahapan, dimulai dengan mengekstraksi ciri citra jalan kemudian ditraining dengan pemberian label manual. Kemudian dilakukan uji data citra berdasarkan nilai pada data training. Dari pengujian Confusion Matrix menunjukkan hasil Recall sebesar 0,80 hasil Precission sebesar 0,06 hasil Accuracy sebesar 0,79 dan hasil Error Rate sebesar 0,20.

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Sri Aprillia, Bandiyah, Achmad Rizal, and Muhammad Arik Geraldy Fauzi. "Grey Level Differences Matrix for Alcoholic EEG Signal Classification." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024): 26. http://dx.doi.org/10.62527/joiv.8.1.2602.

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Electroencephalogram (EEG) signals can provide information on abnormalities in a person's brain and characterize brain activity. Brain injury or diseases can manifest as brain disorders. Trauma or the use of specific chemicals or medications, such as alcohol, can result in brain damage. Previous research has demonstrated variations in the patterns of EEG signals between alcohol-using and non-drinking people. Various techniques, including wavelet and entropy, have been developed to detect alcoholic EEG using event-related potential (ERP) testing. This work proposes a feature extraction technique based on texture analysis for the classification of alcohol EEG signals because ERP-measured EEG often involves many channels. An NxM image is thought to be equivalent to an EEG signal with N channels and a recording duration of M samples. The NxM matrix is formed by channelizing the N-channel EEG signal in this investigation. Normalization is then used to get a matrix value of 0-255 or an 8-bit image in the following step. Five features are measured in four directions, and the Grey Level Difference Matrix (GLDM) approach is utilized for feature extraction. Using five grey-level difference matrix (GLDM) features and linear discriminant analysis as a classifier, the maximum accuracy was achieved at 73.3%. Image processing can still be used to increase accuracy even though the final product is less accurate than the earlier technique. The suggested approach can still be adjusted to work with biomedical signals or image processing techniques like the Grey Level Co-occurrence Matrix (GLCM).

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Chen, Ying, and Feng Yu Yang. "Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix." Applied Mechanics and Materials 204-208 (October 2012): 4746–50. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4746.

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Gray level co-occurrence matrix (GLCM) is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction, local neighborhood and magnitude of changes. Firstly, we analyze and reveal the generation process of gray level co-occurrence matrix from horizontal, vertical and principal and secondary diagonal directions. Secondly, we use Brodatz texture images as samples, and analyze the relationship between non-zero elements of gray level co-occurrence matrix in changes of both direction and distances of each pixels pair by. Finally, we explain its function of the analysis process of texture. This paper can provided certain referential significance in the application of using gray level co-occurrence matrix at quality evaluation of texture image.

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Jain, Palak. "FPGA-Based Satellite Vision Systems using Verilog HDL." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 879–84. http://dx.doi.org/10.22214/ijraset.2024.58850.

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Abstract: It is believed that the most effective way to collect information about the Earth's surface is through high- quality satellite images. Extracting a feature from an image is really difficult because you have to choose the best image segmentation methods and combine many strategies to find the Region in the most effective manner. This study makes recommendations for the classification techniques for objects in the satellite. On high-resolution satellite images, applying image processing methods. The methods used to define region mostly focus on urban, agricultural, and forest regions. There are several methods for extracting these traits. Using a Grey Level Cooccurrence Matrix is the most used method. It is employed to unveil specific characteristics regarding the spatial arrangement of gray levels in the texture image. The Grey Level Co-occurrence Matrix (GLCM) captures statistical details of neighboring pixels in an image, enabling the computation of textural features that enhance the comprehension of visual content. This research presents a VLSI implementation aimed at extracting four texture characteristics from the grey level co-occurrence matrix. Verilog was employed to model the hardware, with MATLAB used for software simulation. The simulation utilized the Verilog HDL language from the XILINX tool, and the implementation was executed on the SPARTAN FPGA board.

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Ramadhani, Faundra Zahwa, Hari Purwadi, and Ansar Rizal. "Clustering K-Means Berdasarkan Ciri Gray Level Co-occurrence Matrix Pada Foto Wajah." Jurnal Komputer, Informasi dan Teknologi 5, no. 1 (2025): 10. https://doi.org/10.53697/jkomitek.v5i1.2498.

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Penelitian ini bertujuan untuk mengetahui Ciri Citra Wajah Berdasarkan Gray Level Coocurence Matrik (Glcm). Dan melakukan pengelompokan klaster menggunakan metode Simpel K- Means untuk mengetahui clustered instances. Tahapan dimulai dari pengambilan gambar wajah menggunakan kamera smartphone dengan jarak 100cm dan pencahayaan yang dikendalikan. Gambar kemudian diubah dari format RGB ke grayscale sebagai langkah awal pengolahan. Matriks GLCM dibentuk berdasarkan empat orientasi sudut (0°, 45°, 90°, dan 135°), dan dari matriks tersebut diekstraksi empat ciri tekstur utama, yaitu contrast, correlation, energy, dan homogeneity. Ekstraksi fitur ini berfungsi sebagai representasi ciri setiap gambar wajah. Data yang digunakan terdiri atas 60 gambar, diperoleh dari 15 pengambilan gambar untuk masing-masing dari 3 subjek yang berbeda, yang di Analisa mengunakan Glcm mendapatkan prototype data ciri sebanyak 60. Seluruh data ciri selanjutnya dianalisis menggunakan algoritma Simpel K-Means, dengan pengujian sebanyak 1-10 cluster yang berbeda di dapatkan Clustered Instances rata – rata sebanyak 56 %.

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Abdulla, Beshaier A., Yossra H. Ali, and Nuha J. Ibrahim. "Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments." Engineering and Technology Journal 38, no. 5A (2020): 719–27. http://dx.doi.org/10.30684/etj.v38i5a.519.

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In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used. And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

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Bhakti, Adhitiyah Redaya Kusuma, Abduh Riski, and Ahmad Kamsyakawuni. "Sistem Biometrik Pengenalan Wajah dengan Metode Grey Level Co-Occurrence Matrix dan Support Vector Machine." Indonesian Journal of Applied Informatics 7, no. 2 (2024): 112. http://dx.doi.org/10.20961/ijai.v7i2.69069.

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Abstrak Teknologi biometrik wajah dikembangkan untuk mengenali seseorang secara unik. Pada penelitian ini biometrik diaplikasikan pada aplikasi pengenalan wajah dengan citra wajah manusia sebagai objeknya menggunakan metode Grey Level Co-Occurrence Matrix dan Support Vector Machine. Metode GLCM merupakan metode yang digunakan untuk proses ekstraksi fitur citra. Sedangkan SVM digunakan untuk proses pengenalan/identifikasi. Tujuan dari penelitian ini adalah mendapat hasil akurasi yang baik untuk pengenalan wajah melalui kedua metode yang digunakan. Hasil yang diperoleh dari penelitian ini adalah akurasi pada data pelatihan sebesar 93% dengan total 200 citra wajah. Sedangkan pada data pengujian diperoleh akurasi sebesar 90% untuk 50 citra wajah.===================================================AbstractFacial biometric technology was developed to uniquely recognize a person. In this research, biometrics was applied to face recognition applications with human face images as objects using the Gray Level Co-Occurrence Matrix and Support Vector Machine methods. The GLCM is a method used for the image feature extraction process. While SVM is used for the identification process. The purpose of this research is to get good accuracy results for face recognition through the two methods used. The results obtained from this research are the accuracy of the training data by 93% with a total of 200 face images. While the test data obtained an accuracy of 90% for 50 face images.

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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 to achieve accurate classification. After training, the ANN model is tested using split test data to measure classification performance. The experimental results show that the GLCM method is effective in helping improve accuracy, validation of accuracy, loss, validation of loss, precision, and recall.

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A. Rahim, Abd Mizwar, and Theopilus Bayu Sasongko. "Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 24, no. 2 (2025): 219–34. https://doi.org/10.30812/matrik.v24i2.4035.

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This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants.

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Blanco, A. C., J. B. Babaan, J. E. Escoto, and C. K. Alcantara. "MODELLING OF LAND SURFACE TEMPERATURE USING GRAY LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST REGRESSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 23–28. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-23-2020.

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Abstract. Modelling of land surface temperature (LST) is conducted to be able to explain the spatial and temporal variations of LST using a set of explanatory variables. LST in a previous study was modelled as a linear function of vegetation cover and built up cover as quantified by the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI), respectively, and other variables, namely, albedo, solar radiation (SR), surface area-volume ratio (SVR), and skyview factor (SVF). SVF requires a digital surface model of sufficient resolution while SVR computation needs 3D volumetric features representing buildings as input. These inputs are typically not readily available. In addition, NDVI and NDBI do not fully describe the spatial variability of vegetation and built-up cover within an LST pixel. In this study, PlanetScope images (3m resolution) were processed to provide soil-adjusted vegetation index (SAVI) and VgNIR Built-up Index (VgNIR-BI) layers. The following gray level co-occurrence matrices (GLCM) were generated from SAVI and VgNIR-BI: Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, and Correlation. Random Forest regression was run for several cases with different combinations of GLCM features and non-GLCM variables. Using GLCM features alone yielded less satisfactory models. However, the use of additional GLCM features in combination with other variables resulted in lower MSE and a slight increase in R2. Considering NDBI, NDVI, SAVI_GLCM_contrast, VgNIR-BI_GLCM_contrast, VgNIR-BI_GLCM_dissimilarity, and SAVI_GLCM_contrast only, the RF model yielded an MSE=1.657 and validation R2=0.822. While this 6-variable model’s performance is slightly less, the need for DSM and 3D building models which are necessary for the generation of SVF and SVR layers is eliminated. Exploratory regression (ER) was also conducted. The best 6-variable ER model (Adj. R2=0.79) consists of SVR, NDBI, NDVI, SAVI_GLCM_second_moment, VgNIR-BI_GLCM_mean, and VgNIR-BI_GLCM_entropy. In comparison, OLS regression using the 6 non-GLCM variables yielded an Adj. R2=0.691. The results of RFR and ER both indicate the value of GLCM features in providing valuable information to the models of LST. LST is best described through a combination of GLCM features describing relatively homogenous areas (i.e., dominant land cover or low-frequency areas) and the more heterogenous areas (i.e., edges or high-frequency areas) and non-GLCM variables.

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PANTIC, IGOR, SENKA PANTIC, JOVANA PAUNOVIC, and MILAN PEROVIC. "Nuclear entropy, angular second moment, variance and texture correlation of thymus cortical and medullar lymphocytes: Grey level co-occurrence matrix analysis." Anais da Academia Brasileira de Ciências 85, no. 3 (2013): 1063–72. http://dx.doi.org/10.1590/s0001-37652013005000045.

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Grey level co-occurrence matrix analysis (GLCM) is a well-known mathematical method for quantification of cell and tissue textural properties, such as homogeneity, complexity and level of disorder. Recently, it was demonstrated that this method is capable of evaluating fine structural changes in nuclear structure that otherwise are undetectable during standard microscopy analysis. In this article, we present the results indicating that entropy, angular second moment, variance, and texture correlation of lymphocyte nuclear structure determined by GLCM method are different in thymus cortex when compared to medulla. A total of 300 thymus lymphocyte nuclei from 10 one-month-old mice were analyzed: 150 nuclei from cortex and 150 nuclei from medullar regions of thymus. Nuclear GLCM analysis was carried out using National Institutes of Health ImageJ software. For each nucleus, entropy, angular second moment, variance and texture correlation were determined. Cortical lymphocytes had significantly higher chromatin angular second moment (p < 0.001) and texture correlation (p < 0.05) compared to medullar lymphocytes. Nuclear GLCM entropy and variance of cortical lymphocytes were on the other hand significantly lower than in medullar lymphocytes (p < 0.001). These results suggest that GLCM as a method might have a certain potential in detecting discrete changes in nuclear structure associated with lymphocyte migration and maturation in thymus.

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Albkosh, Fthi M. A., Alsadegh S. S. Mohamed, Ali A. Elrowayati, and Mamamer M. Awinat. "Features Optimization of Gray Level Co-Occurrence Matrix by Artificial Bee Colony Algorithm for Texture Classification." مجلة الجامعة الأسمرية: العلوم التطبيقية 6, no. 5 (2021): 839–57. http://dx.doi.org/10.59743/aujas.v6i5.1294.

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Gray Level Co-occurrence Matrix (GLCM) is one of the most popular texture analysis methods. The fundamental issue of GLCM is the suitable selection of input parameters, where many researchers depended on trial and observation approach for selecting the best combination of GLCM parameters to improve the texture classification, which is tedious and time-consuming. This paper proposes a new optimization method for the GLCM parameters using Artificial Bee Colony Algorithm (ABC) to improve the binary texture classification. For the testing, 13 Haralick features were extracted from the UMD database, which has been used with the multi-layer perceptron neural network classifier. The experimental results proved that, the proposed method has been succeeded to finding the best combination of GLCM parameters that leads to the best binary texture classification accuracy performance.

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Mrs.S.Gandhimathi, @. Usha, Rani M.Jeya, S.Sneha, and R.Kalaiselvi. "FEATURE EXTRACTION BASED RETRIEVAL OF GEOGRAPHIC IMAGES." International Journal of Computational Science and Information Technology (IJCSITY) 2, May (2019): 1–9. https://doi.org/10.5281/zenodo.3532468.

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ABSTRACT This project is to retrieve the similar geographic images from the dataset based on the features extracted. Retrieval is the process of collecting the relevant images from the dataset which contains more number of images. Initially the preprocessing step is performed in order to remove noise occurred in input image with the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the features from the images. After this process, the relevant geographic images are retrieved from the dataset by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to perform reliable recognition, it is important that the feature extracted from the training image be detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in the applications such as detection and classification. KEYWORDS GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM), SCALE INVARIANT FEATURE TRANSFORM (SIFT)

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Shelke, Mrs Vishakha, Mr Vinay Manish Shah, Mr Harsh Ratnani, and Mr Rahul Despande. "Diabetic Retinopathy Detection Using SVM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 868–75. http://dx.doi.org/10.22214/ijraset.2022.41275.

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Abstract: Innovation is getting progressed step by step in pretty much every field. This work includes the detection of Diabetic Retinopathy (DR). Diabetes happens when the pancreas neglects to emit sufficient insulin, and gradually influences the retina of the natural eye. As it advances, the vision of a patient begins deteriorating (depleting), prompting diabetic retinopathy. In such manner, retinal pictures gained through fundal camera help in investigating the outcomes, nature, and status of the impact of diabetes on the eye. The main aim of this study is Age-related Macular Degeneration (AMD) through Local Binary Patterns (LBP) and further trial and error utilizing Gray-Level Co-Occurrence Matrix (GLCM). For this reason, the presentation of Gray level Co-Occurrence Matrix (GLCM) as a surface descriptor for retinal pictures has been investigated and contrasted and different descriptors, for example, GLCM filtering (GLCMF) and local phase quantization (LPQ). This will take to the of blood vessel highlights, for example, energy, differentiation, correlation and homogeneity values. We involve SVM as a classifier to recognize valid and bogus vessels. The conclusion of diabetic retinopathy depends on clinical eye assessment and eye fundus imaging. Keywords: Machine Learning, Support Vector Machine, Mat lab, Histogram Equalization

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MUSIAFA, ZAYID. "PERANCANGAN EKSTRAKSI FITUR MOTIF SASIRANGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES BERBASIS COLOR HISTOGRAM DAN GRAY LEVEL CO-OCCURRENCE MATRICES (GLCM)." Technologia: Jurnal Ilmiah 8, no. 2 (2017): 108. http://dx.doi.org/10.31602/tji.v8i2.1114.

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Sasirangan adalah kain adat suku Banjar di Kalimantan Selatan yang dibuat dengan teknik tusuk jelujur. Penelitian menggunakan uji algoritma Naive Bayes Klasifikasi terhadap citra kain sasirangan yang diekstrak dengan metode berbasis color histogram dan GLCM data terdiri dari 30 citra digital kain sasirangan terdiri dari 10 data citra motif Hiris Gagatas dengan label g class 0, 10 data citra motif Kulat Kurikit diberi label k class 1, dan 10 data citra motif Absrak diberi label a class 2. Pengujian data menggunakan X-Validation dengan ketentuan Number Validaton uji 10 sampai dengan 2, type validasi yang diuji mulai dari Stratified Sampling, Shuffled Sampling dan Linier Sampling. Kesimpulan dari penelitian yang dilakukan diperoleh hasil accuracy Stratified Sampling Color Histogram dengan nilai validasi 5 miliki nilai tertinggi dibandingkan Shuffled Sampling dan Linier Sampling dengan accuracy 63.33%. Hasil accuracy Stratified Sampling GLCM 0°, GLCM 45°, dan GLCMRata-rata dengan nilai validasi 3 miliki nilai tertinggi dengan accuracy 80.00%. Sedangkan hasil accuracy Stratified Sampling GLCM 90° dengan nilai validasi 3 dan Accuracy Linier Sampling nilai validasi 10 miliki nilai tertinggi dengan accuracy 73.33%. Hasil accuracy Stratified Sampling GLCM 135° dengan nilai validasi 3 miliki accuracy 76.67%. Kata Kunci : Sasirangan, Naive Bayes, Klasifikasi, Color Histogram, Grey Level Coocurrence Matrix (GLCM)

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Yan, L., and W. Xia. "A MODIFIED THREE-DIMENSIONAL GRAY-LEVEL CO-OCCURRENCE MATRIX FOR IMAGE CLASSIFICATION WITH DIGITAL SURFACE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 133–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-133-2019.

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Abstract. 2D texture cannot reflect the 3D object’s texture because it only considers the intensity distribution in the 2D image region but int real world the intensities of objects are distributed in 3D surface. This paper proposes a modified three-dimensional gray-level co-occurrence matrix (3D-GLCM) which is first introduced to process volumetric data but cannot be used directly to spectral images with digital surface model because of the data sparsity of the direction perpendicular to the image plane. Spectral and geometric features combined with no texture, 2D-GLCM and 3D-GLCM were put into random forest for comparing using ISPRS 2D semantic labelling challenge dataset, and the overall accuracy of the combination containing 3D GLCM improved by 2.4% and 1.3% compared to the combinations without textures or with 2D-GLCM correspondingly.

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ARORA, VINAY, EDDIE YIN-KWEE NG, ROHAN SINGH LEEKHA, KARUN VERMA, TAKSHI GUPTA, and KATHIRAVAN SRINIVASAN. "HEALTH OF THINGS MODEL FOR CLASSIFYING HUMAN HEART SOUND SIGNALS USING CO-OCCURRENCE MATRIX AND SPECTROGRAM." Journal of Mechanics in Medicine and Biology 20, no. 06 (2020): 2050040. http://dx.doi.org/10.1142/s0219519420500402.

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Cardiovascular diseases have become one of the world’s leading causes of death today. Several decision-making systems have been developed with computer-aided support to help the cardiologists in detecting heart disease and thereby minimizing the mortality rate. This paper uses an unexplored sub-domain related to textural features for classifying phonocardiogram (PCG) as normal or abnormal using Grey Level Co-occurrence Matrix (GLCM). The matrix has been applied to extract features from spectrogram of the PCG signals taken from the Physionet 2016 benchmark dataset. Random Forest, Support Vector Machine, Neural Network, and XGBoost have been applied to assess the status of the human heart using PCG signal spectrogram. The result of GLCM is compared with the two other textural feature extraction methods, viz. structural co-occurrence matrix (SCM), and local binary patterns (LBP). Experimental results have proved that applying machine learning model to classify PCG signal on the dataset where GLCM has extracted the feature-set, the accuracy attained is greater as compared to its peer approaches. Thus, this methodology can go a long way to help the medical specialists in precisely and accurately assessing the heart condition of a patient.

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Li, Min, and Jian Jun Liao. "Texture Image Segmentation Based on GLCM." Applied Mechanics and Materials 220-223 (November 2012): 1398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1398.

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The paper proposed a method on marble texture image segmentation based on Gray Level Co-occurrence Matrix (GLCM). At first, compute the Contrast matrix on basis of GLCM. Then choose the maximum of the matrix as the threshold to segment the object. At last extract the object contour with curve fitting method. Experiment results show that the method is accuracy.

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Shi, Huilan, Junya Jia, Dong Li, Li Wei, Wenya Shang, and Zhenfeng Zheng. "Blood oxygen level-dependent magnetic resonance imaging for detecting pathological patterns in patients with lupus nephritis: a preliminary study using gray-level co-occurrence matrix analysis." Journal of International Medical Research 46, no. 1 (2017): 204–18. http://dx.doi.org/10.1177/0300060517721794.

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Objective Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) is a noninvasive technique useful in patients with renal disease. The current study was performed to determine whether BOLD MRI can contribute to the diagnosis of renal pathological patterns. Methods BOLD MRI was used to obtain functional magnetic resonance parameter R2* values. Gray-level co-occurrence matrixes (GLCMs) were generated for gray-scale maps. Several GLCM parameters were calculated and used to construct algorithmic models for renal pathological patterns. Results Histopathology and BOLD MRI were used to examine 12 patients. Two GLCM parameters, including correlation and energy, revealed differences among four groups of renal pathological patterns. Four Fisher’s linear discriminant formulas were constructed using two variables, including the correlation at 45° and correlation at 90°. A cross-validation test showed that the formulas correctly predicted 28 of 36 samples, and the rate of correct prediction was 77.8%. Conclusions Differences in the texture characteristics of BOLD MRI in patients with lupus nephritis may be detected by GLCM analysis. Discriminant formulas constructed using GLCM parameters may facilitate prediction of renal pathological patterns.

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Prakash, Shubhra, and B. Ramamurthy. "Gray Level Co-occurrence Matrix based Fully Convolutional Neural Network Model for Pneumonia Detection." International journal of electrical and computer engineering systems 15, no. 4 (2024): 369–76. http://dx.doi.org/10.32985/ijeces.15.4.7.

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This study presents a new method to improve the detection ability of a convolutional neural network (CNN) in pneumonia detection using chest X-ray images. Using Gray-Level Co-occurrence Matrix (GLCM) analysis, additional channels are added to the original image data provided by Guangzhou Children's Hospital in Guangzhou, China. The main goal is to design a lightweight, fully convolution network and increase its available information using GLCM. Performance analysis is performed on the new CNN model and GLCM-enhanced CNN model, and results are compared with Transfer Learning approaches. Various evaluation metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, are used to evaluate the improved analysis performance of CNN. The results showed a significant increase in the ability of the model to detect pneumonia, with an accuracy of 99.57%. In addition, the study evaluates the descriptive properties of the CNN model by analyzing its decision process using Grad-CAM.

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Isnanto, R. Rizal, Munawar Agus Riyadi, and Muhammad Fahmi Awaj. "Herb Leaves Recognition using Gray Level Co-occurrence Matrix and Five Distance-based Similarity Measures." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1920. http://dx.doi.org/10.11591/ijece.v8i3.pp1920-1932.

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Herb medicinal products derived from plants have long been considered as an alternative option for treating various diseases. In this paper, the feature extraction method used is Gray Level Co-occurrence Matrix (GLCM), while for its recognition using the metric calculations of Chebyshev, Cityblock, Minkowski, Canberra, and Euclidean distances. The method of determining the GLCM Analysis based on the texture analysis resulting from the extraction of this feature is Angular Second Moment, Contrast, Inverse Different Moment, Entropy as well as its Correlation. The recognition system used 10 leaf test images with GLCM method and Canberra distance resulted in the highest accuracy of 92.00%. While the use of 20 and 30 test data resulted in a recognition rate of 50.67% and 60.00%.

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Anggraini, Chintya, and Sriani. "KLASIFIKASI DAUN KELENGKENG MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN K-NEAREST NEIGHBOR (KNN)." JSiI (Jurnal Sistem Informasi) 11, no. 2 (2024): 72–78. http://dx.doi.org/10.30656/jsii.v11i2.9157.

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Tanaman kelengkeng (Dimocarpus longan) termasuk dalam jenis tanaman buah dengan nilai ekonomi tinggi dan menjadi komoditas penting dalam sektor pertanian. Kelengkeng memiliki berbagai varietas yang beragam berdasarkan ciri-ciri khas dari masing-masing jenis yang cukup sulit dibedakan, terutama bagi orang awam. Berdasarkan permasalahan dalam menentukan jenis tanaman kelengkeng, maka perlu adanya sistem yang dapat mengklasifikasikan jenis tanaman kelengkeng. Penelitian ini mengusulkan ekstraksi fitur tekstur dari citra daun kelengkeng dengan memanfaatkan Gray Level Co-occurrence Matrix (GLCM), dengan fitur contrast, correlation, homogeneity, dan energy, yang selanjutnya diklasifikasikan dengan algoritma K-Nearest Neighbor (KNN). Metode ini diterapkan pada dataset citra daun dari berbagai varietas kelengkeng, yaitu aroma durian, diamond river, pingpong, dan kelengkeng merah. Metode ini diterapkan untuk meningkatkan akurasi dan efisiensi dalam klasifikasi jenis kelengkeng. Hasil penelitian menunjukkan bahwa penerapan GLCM dan KNN berhasil dilakukan dengan akurasi klasifikasi mencapai 87,5%. Dari 16 citra uji, 14 citra berhasil diklasifikasikan dengan benar. Kata Kunci: Daun, Kelengkeng, Gray Level Co-occurrence Matrix (GLCM), K-Nearest Neighbor (KNN)

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Chen, Zi Xin, and Feng Yu Xu. "Application of Gray Level Co-Occurrence Matrix Method in Characterization of Cylindrical Grinding Surface Roughness." Applied Mechanics and Materials 433-435 (October 2013): 2113–16. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2113.

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Machine vision based surface roughness inspection method is applied to assess different cylindrical grinding surfaces under LED illumination. Images directly recorded by a camera are analyzed by gray level co-occurrence matrix (GLCM) method to discover its texture information. It shows obviously relationship between feature values of the matrix and their corresponding surface roughness values. Uniform table are also designed to choose optimal parameters, which is five of distance between pixel pairs and ninety degree of angle to calculate GLCM. Entropy is chosen to represent different surface roughness images by comparison of correlation coefficients between the parameters and the corresponding surface roughness values.

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Salsabiilaa, Rizka Kaamtsaalil. "DETEKSI KUALITAS DAN KESEGARAN TELUR AYAM RAS BERDASARKAN DETEKSI OBJEK TRANSPARAN DENGAN METODE GREY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN KLASIFIKASI K-NEAREST NEIGHBOR (KNN)." TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika 1, no. 2 (2019): 1. http://dx.doi.org/10.25124/tektrika.v1i2.1740.

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Telur adalah salah satu bahan pangan yang mudah dan lazim dijumpai di masyarakat Indonesia. Selain harganya murah, telur merupakan sumber nutrisi penting bagi kesehatan tubuh. Namun telur memiliki kualitas dan kesegaran yang berbeda-beda tergantung pada lingkungan penyimpanan dan kondisi induknya. Kesegaran telur dapat diketahui dari ketebalan dan kekentalan putih telurnya. Semakin tinggi putih telur semakin segar telur tersebut. Tebal atau tinggi albumen dapat diketahui dari nilai HU (Haugh Unit). Dalam makalah ini penulis membahas mengenai cara mendeteksi kualitas dan kesegaran telur menggunakan deteksi objek transparan dengan menggunakan metode GLCM (Grey Level Co-occurrence Method) dan klasifikasi KNN (K-Nearest Neighbor). Telur yang digunakan ialah telur ayam negeri. Pada penelitian ini dilakukan pengujian 51 citra telur, dengan komposisi masing-masing kelas memiliki 17 citra telur AA, 17 citra telur A, dan 17 citra telur B. Sehingga didapatkan akurasi terbaik sebesar 82.35% dengan menggunakan metode GLCM (Grey Level Co-occurrence Matrix) dengan parameter orde dua kontras, energy, korelasi, homogenitas dan arah sudut 45 pada jarak d = 1 dan kuantisasi yang digunakan adalah 8, dengan klasifikasi KNN (K-Neirest Neighbor) menggunakan jarak cosine pada K= 1.

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Zhu, Dandan, Ruru Pan, Weidong Gao, and Jie Zhang. "Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM." Autex Research Journal 15, no. 3 (2015): 226–32. http://dx.doi.org/10.1515/aut-2015-0001.

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Abstract In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

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Arifin, Arifin, and Abdul Rahman. "Identifikasi Kualitas Kerabang Telur Ayam Dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix." MDP Student Conference 2, no. 1 (2023): 250–56. http://dx.doi.org/10.35957/mdp-sc.v2i1.4276.

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Telur merupakan salah satu hasil ternak yang mengandung komponen gizi yang lengkap seperti protein, lemak, vitamin dan mineral. Selain itu, telur juga banyak diminati masyarakat dikarenakan harga nya yang terjangkau dan mempunyai ketersediaan yang cukup banyak. Namun tidak semua telur mempunyai kualitas yang baik, sehingga pada penelitian ini dilakukan identifikasi kualitas kerabang telur dimana kualitas kerabang telur dibagi menjadi tiga golongan yakni Gol 1,Gol 2, dan Gol 3 untuk tiap jarak potret 8 cm, 12 cm dan 16 cm yang kemudian menerapkan Gray Level Co-Occurrence Matrix (GLCM) sebagai ekstraksi fitur dan metode Jaringan Syaraf Tiruan (JST). Hasil dari ekstraksi GLCM dijadikan masukan pada proses pembelajaran pada JST dengan training function trainlm. Hasil akurasi yang paling baik dicapai pada jarak potret 12 cm yaitu sebesar 44.44% dan jumlah pengenalan data uji sebanyak 12 dari 27 data uji.

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Journal articles on the topic 'Grey Level Co-Occurrence Matrix (GLCM)'

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