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Saihood, Ahmed, Hossein Karshenas, and Ahmad Reza Naghsh Nilchi. "Deep fusion of gray level co-occurrence matrices for lung nodule classification." PLOS ONE 17, no. 9 (September 29, 2022): e0274516. http://dx.doi.org/10.1371/journal.pone.0274516.
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Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.
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Goel, Sajal Kumar, and Mrudula Meduri. "Ear Biometric System using GLCM Algorithm." International Journal of Information Technology and Computer Science 9, no. 10 (October 8, 2017): 68–76. http://dx.doi.org/10.5815/ijitcs.2017.10.07.
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Pietri, Olivia, Gada Rezgui, Aymeric Histace, Marine Camus, Isabelle Nion-Larmurier, Cynthia Li, Aymeric Becq, et al. "Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy." Endoscopy International Open 06, no. 04 (March 29, 2018): E462—E469. http://dx.doi.org/10.1055/a-0573-1044.
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Abstract Background and study aims Bubbles can impair visualization of the small bowel (SB) mucosa during capsule endoscopy (CE). We aimed to develop and validate a computed algorithm that would allow evaluation of the abundance of bubbles in SB-CE still frames. Patients and methods Two sets of 200 SB-CE normal still frames were created. Two experienced SB-CE readers analyzed both sets of images twice, in a random order. Each still frame was categorized as presenting with < 10 % or ≥ 10 % of bubbles. Reproducibility (κ), sensitivity (Se), specificity (Sp), receiver operating characteristic curve, and calculation time were measured for different algorithms (Grey-level of co-occurrence matrix [GLCM], fractal dimension, Hough transform, and speeded-up robust features [SURF]) using the experts’ analysis as reference. Algorithms with highest reproducibility, Se and Sp were then selected for a validation step on the second set of frames. Criteria for validation were κ = 1, Se ≥ 90 %, Sp ≥ 85 %, and a calculation time < 1 second. Results Both SURF and GLCM algorithms had high operating points (Se and Sp over 90 %) and a perfect reproducibility (κ = 1). The validation step showed the GLCM detector strategy had the best diagnostic performances, with a Se of 95.79 %, a Sp of 95.19 %, and a calculation time of 0.037 seconds per frame. Conclusion A computed algorithm based on a GLCM detector strategy had high diagnostic performance allowing assessment of the abundance of bubbles in SB-CE still frames. This algorithm could be of interest for clinical use (quality reporting) and for research purposes (objective comparison tool of different preparations).
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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 (February 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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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 (September 1, 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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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 (December 31, 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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Wang, Hui, Shiyu Li, Haiyang Qiu, Zhizhong Lu, Yanbo Wei, Zhiyu Zhu, and Huilin Ge. "Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images." Remote Sensing 15, no. 8 (April 14, 2023): 2078. http://dx.doi.org/10.3390/rs15082078.
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The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.
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Juneja, Jyoti, and Avani Chopra. "GLCM and PCA Algorithm based Watermarking Scheme." International Journal of Computer Applications 180, no. 48 (June 15, 2018): 24–29. http://dx.doi.org/10.5120/ijca2018917261.
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Mittal, Abhishek, Pravneet Kaur, and Dr Ashish Oberoi. "Hybrid Algorithm for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 1028–37. http://dx.doi.org/10.22214/ijraset.2022.40452.
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Abstract: The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that the introduced method performs more efficiently in comparison with the conventional techniques with regard to accuracy. Keywords: Face Spoof, KNN, Hybrid Classifier, GLCM
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Samsudin, S. A., and R. C. Hasan. "ASSESSMENT OF MULTIBEAM BACKSCATTER TEXTURE ANALYSIS FOR SEAFLOOR SEDIMENT CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W5 (October 10, 2017): 177–83. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w5-177-2017.
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Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7&thinsp;km<sup>2</sup>, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77&thinsp;% of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.
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Andono, Pulung Nurtantio, and Eko Hari Rachmawanto. "Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 13, 2021): 1–9. http://dx.doi.org/10.29207/resti.v5i1.2615.
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Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.
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Nikolovski, Dubravka, Jelena Cumic, and Igor Pantic. "Application of Gray Level co-Occurrence Matrix Algorithm for Detection of Discrete Structural Changes in Cell Nuclei After Exposure to Iron Oxide Nanoparticles and 6-Hydroxydopamine." Microscopy and Microanalysis 25, no. 4 (June 18, 2019): 982–88. http://dx.doi.org/10.1017/s1431927619014594.
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AbstractThe gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.
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Li, Hui Na, and Jun Li Luo. "GLCM Inspired Fingerprints Segmentation Algorithm with Adaptive Block Size." Applied Mechanics and Materials 239-240 (December 2012): 1456–61. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1456.
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In order to reduce the dependence on the images' sizes, resolutions and qualities, a self-adaptive block size fingerprint segmentation algorithm based on the gray level co-occurrence matrix (GLCM) is proposed. Firstly, the image is divided into a number of non-overlapped rectangular blocks whose size is automatically determined by the mean of the ridge distance from the spectrogram. Then the contrasts of the GLCM of each block in different directions of pixel-pair could be calculated. Since the variances of these contrasts are different for the foreground and the background, finally, the fingerprint image can be segmented correctly. Experimental results show that the proposed algorithm performs effectively in processing images gathered by various fingerprint sensors in diverse environments.
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Basri, Basri, Muhammad Assidiq, Harli A. Karim, and Andi Nuraisyah. "Extreme Learning Machine with Feature Extraction Using GLCM for Phosphorus Deficiency Identification of Cocoa Plants." ILKOM Jurnal Ilmiah 14, no. 2 (August 31, 2022): 112–19. http://dx.doi.org/10.33096/ilkom.v14i2.1226.112-119.
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This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.
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Dhara, Anwesha, Liana Langdon-Embry, Michael I. D’Angelica, T. Peter Kingham, Natally Horvat, William R. Jarnagin, Alice C. Wei, and Jayasree Chakraborty. "Abstract A040: CT Radiomics to predict early recurrence of margin-negative resectable pancreatic ductal adenocarcinoma." Cancer Research 82, no. 22_Supplement (November 15, 2022): A040. http://dx.doi.org/10.1158/1538-7445.panca22-a040.
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Abstract Radiographic CT/MRI staging is pivotal to accurate therapeutic decision-making, and guides the surgical resectability of pancreatic ductal adenocarcinoma (PDAC). The extent of vascular involvement of the lesion as seen through radiographic imaging, which differentiates the regional versus locally advanced disease, and therefore upfront resectability. Following standard criteria, only about 20% of PDAC patients qualify for an upfront surgery but half of them recur within a year, despite the complete removal (margin-negative R0 resection) of the primary tumor It underscores the necessity for a better deciphering of the CT/MRI-based clinical imaging of PDAC using radiomics—the process of extracting high dimensional quantitative imaging information from radiographic images. We have previously described a prospective cohort of resected PDAC (n=161) at Memorial Sloan Kettering Cancer Center for preoperative CT angiography collected between 2009 and 2012. We demonstrated that CT texture features are associated with overall survival. Utilizing the same cohort of resected PDAC, the current study aims to develop a much deeper case-controlled algorithm to further identify the texture features are predictive of early recurrence for margin-negative PDAC patients. We selected all patients following these criteria: margin negative (R0), Gemcitabine/nab-paclitaxel adjuvant chemotherapy (excluding neoadjuvant), clinical-stage IIB, moderate tumor differentiation by histopathology, and no lymph node and distant metastasis. Based on these criteria, a total of 27 patients were selected, where 16 patients had recurred within a year of surgery (high-risk group) and the remaining 11 patients did not recur in 2-years (low-risk group). We focused on Gray-level co-occurrence (GLCM) based on Haralick’s features. GLCM is most extensively used to characterize the texture of a tumor via encoding the spatial distribution of voxels—neighboring pixels of an image. A total of 18 GLCM features were extracted from manually segmented CT of PDAC primary tumors. Univariate analysis with Wilcoxon-rank-sum test (p<0.05) revealed 4 features –energy (GLCM1) and entropy (GLCM9, GLCM17, and GLCM18) based were associated with early recurrence. Energy, or angular speed moment, is a measure of local variation in an image; higher energy represents homogeneity between neighboring pixels. On the contrary, entropy characterizes randomness within an image; higher entropy represents more heterogeneity. The energy feature GLCM1 was significantly lower in high-risk group, whereas the entropy features (GLCM 9, 17, and 18) were significantly higher in high-risk group compared to the low-risk group – suggesting the relationship of tumor heterogeneity with early recurrence. While proposing these GLCM features as the measure of tumor heterogeneity and early recurrence, we are validating them on a larger independent cohort, estimating their benchmark measures of predictive ability. The long-term goal is to develop these as radiomic biomarkers for better guiding the therapeutic decision-making of PDAC. Citation Format: Anwesha Dhara, Liana Langdon-Embry, Michael I. D’Angelica, T. Peter Kingham, Natally Horvat, William R. Jarnagin, Alice C. Wei, Jayasree Chakraborty. CT Radiomics to predict early recurrence of margin-negative resectable pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A040.
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Benedict Jose, T. J., and P. Eswaran. "An Efficient Steganalytic Algorithm based on Contourlet with GLCM." Research Journal of Applied Sciences, Engineering and Technology 8, no. 12 (September 25, 2014): 1396–403. http://dx.doi.org/10.19026/rjaset.8.1113.
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Pai, A. G., K. M. Buddhiraju, and S. S. Durbha. "QUANTUM INSPIRED GENETIC ALGORITHM FOR BI-LEVEL THRESHOLDING OF GRAY-SCALE IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W6-2022 (February 23, 2023): 483–88. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w6-2022-483-2023.
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Abstract. Thresholding is the primitive step in the process of image segmentation. Finding the optimal threshold for satellite images with reduced computation time and resources is still a challenging task. In this paper, we propose a Grey-Level Co-occurrence Matrix based Quantum Inspired Genetic Algorithm (QGA-GLCM) for bi-level thresholding of gray-scale images (natural and satellite). In this paper, QGA was used to find the optimal threshold. The results are compared with four different variants of Differential Evolution (DE) meta-heuristic algorithms, namely- DE-Otsu, DE-Kapur, DE-Tsali’s, DE-GLCM, and three different variants of QGA, namely- QGA-Otsu, QGA-Kapur, QGA-Tsali’s. Intensity value from image pixel is the only information used by Otsu, Tsali’s and Kapur for thresholding and are highly affected by noise. The main objective of this paper was a) To have a binary threshold for images corrupted with noise by bringing in spatial context b) To reduce the computational complexity and time for generating a threshold. Performance evaluators viz., CPU time, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) were used for quantitative assessment of partitioned images. From this study we observed that our proposed technique, QGA-GLCM is a) very good at producing a diverse population b) ten times faster than its classical counterparts c) generates better threshold for images corrupted by noise. In general, the threshold values generated by QGA and its variants are better than its classical counterparts. The results clearly show that exploration and exploitation capability of QGA is superior to DE for all variants. QGA-GLCM can be an effective technique to generate thresholds both in terms of computational speed and time.
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AZMI, FADHILLAH, Amir Saleh, and N. P. Dharshinni. "Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 203–11. http://dx.doi.org/10.31289/jite.v4i1.3885.
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Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.
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Yinka Ogundepo, Oludare, Isaac Ozovehe Avazi Omeiza, and Jonathan Ponmile Oguntoye. "Optimized textural features for mass classification in digital mammography using a weighted average gravitational search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5001. http://dx.doi.org/10.11591/ijece.v12i5.pp5001-5013.
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Early detection of breast cancer cells can be predicted through a precise feature extraction technique that can produce efficient features. The application of Gabor filters, gray level co-occurrence matrices (GLCM) and other textural feature extraction techniques have proven to achieve promising results but were often characterized by a high false-positive rate (FPR) and false-negative rate (FNR) with high computational complexities. This study optimized textural features for mass classification in digital mammography using the weighted average gravitational search algorithm (WA-GSA). The Gabor and GLCM features were fused and optimized using WA-GSA to overcome the weakness of the textural feature techniques. With support vector machine (SVM) used as the classifier, the proposed algorithm was compared with commonly applied techniques. Experimental results show that the SVM with WA-GSA features achieved FPR, FNR and accuracy of 1.60%, 9.68% and 95.71% at 271.83 s, respectively. Meanwhile, SVM with Gabor features achieved FPR, FNR and accuracy of 3.21%, 12.90% and 93.57% at 2351.29 s, respectively, while SVM with GLCM features achieved FPR, FNR and accuracy of 4.28%, 18.28% and 91.07% at 384.54 s, respectively. The obtained results show the prevalence of the proposed algorithm, WA-GSA, in the classification of breast cancer tumor detection.
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Tong, Kuangwei, Zhongbin Wang, Lei Si, Chao Tan, and Peiyang Li. "A Novel Pipeline Leak Recognition Method of Mine Air Compressor Based on Infrared Thermal Image Using IFA and SVM." Applied Sciences 10, no. 17 (August 29, 2020): 5991. http://dx.doi.org/10.3390/app10175991.
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In order to accurately identify the pipeline leak fault of a mine air compressor, a novel intelligent diagnosis method is presented based on the integration of an adaptive wavelet threshold denoising (WTD) algorithm, improved firefly algorithm (IFA), Otsu-Grabcut image segmentation algorithm, histogram of oriented gradient (HOG), gray-level co-occurrence matrix (GLCM) and support vector machine (SVM). In the proposed method, the adaptive step strategy and local optimal firefly self-search strategy for the basic firefly algorithm (FA) are used to improve the optimization effect. The infrared thermal image is denoised by using wavelet threshold algorithm which is optimized by IFA (WTD-IFA). The Otsu-Grabcut algorithm is used to segment the image and extract the target. The HOG and GLCM are calculated to reveal the intrinsic characteristics of the infrared thermal image to extract feature vectors. Then the IFA is utilized to optimize the parameters of SVM so as to construct an optimal classifier for fault diagnosis. Finally, the proposed fault diagnosis method is fully evaluated by experimentation and the results verify its feasibility and superiority.
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Hadinegoro, Arifiyanto, and Dicky Andhika Rizaldilhi. "Pengaruh HSV Pada Pengolahan Citra Untuk Kematangan Buah Cabai." Building of Informatics, Technology and Science (BITS) 3, no. 3 (December 31, 2021): 155–63. http://dx.doi.org/10.47065/bits.v3i3.1020.
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Image or image recognition has quite complex stages so that the computer is able to recognize what objects to identify, there are several stages used in the image processing process, image extraction data input and image classification, this study will examine the extent to which the algorithm for extraction have an influence on the final classification results. This study uses the image of chili as a test material and the results of the test are the introduction of chili with 4 levels, ripe, half cooked, raw and rotten. This study also uses tools to perform the image recognition process. The image recognition method used in this study is Hue Saturation Value (HSV) Gray Level Co-occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ3) for classification, in the process it will be tested. With 2 image recognition scenarios, the first method uses GLCM and LVQ3, the second scenario uses HSV GLCM and LVQ3. The results of this study are to see how much influence HSV has on the digital image recognition process of chili, the extraction results using GLCM extraction LVQ3 classification is 59.58%, and the results of LVQ3 classification using HSV and GLCM extraction yields 93.58%. In conclusion, the HSV extraction method provides an additional 34% accuracy compared to using only GLCM as the extraction method
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Thanoon, Kifaa. "Proposed Algorithm For Using GLCM Properties To Distinguishing Geometric Shapes." AL-Rafidain Journal of Computer Sciences and Mathematics 13, no. 1 (January 2, 2020): 32–47. http://dx.doi.org/10.33899/csmj.2020.163501.
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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 (March 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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Guna, Putu Wahyu Tirta, and Luh Arida Ayu Ayu Rahning Putri. "Endek Classification Based On GLCM Using Artificial Neural Networks with Adam Optimization." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (November 24, 2020): 285. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p16.
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Not many people know that endek cloth itself has 4 known variances. .Nowadays. Computing and classification algorithm can be implemented to solve classification problem with respect to the features data as input. We can use this computing power to digitalize these endek pattern. The features extraction algorithm used in this research is GLCM. Where these data will act as input for the neural network model later. There is a lot of optimizer algorithm to use in back propagation phase. In this research we prefer to use adam which is one of the newest and most popular optimizer algorithm. To compare its performace we also use SGD which is older and popular optimizer algorithm. Later we find that adam algorithm generate 33% accuracy which is better than what SGD algorithm give, it is 23% accuracy. Longer epoch also give affect for overall model accuracy.
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Prakash, Sunkavalli Jaya, Manna Sheela Rani Chetty, and Jayalakshmi A. "Contrast Enhancement of Images Using Meta-Heuristic Algorithm." Traitement du Signal 38, no. 5 (October 31, 2021): 1345–51. http://dx.doi.org/10.18280/ts.380509.
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One of the most important processes in image processing is image enhancement, which aims to enhance image contrast and quality of information. Due to the lack of adequate conventional image enhancement and the challenge of mean shift, intelligence-based image enhancement systems are becoming an essential requirement in image processing. This paper proposes a new approach for enhancing low contrast images utilizing a modified measure and integrating a new Chaotic Crow Search (CCS) and Krill Herd (KH) Optimization-based metaheuristic algorithm. Crow Search Algorithm is a cutting-edge meta-heuristic optimization technique. Chaotic maps are incorporated into the Crow Search Method in this work to improve its global optimization. The new Chaotic Crow Search Algorithm depends on chaotic sequences to replace a random location in the search space and the crow's recognition factor. Based on a new fitness function, Krill Herd optimization is utilized to optimize the tunable parameter. The fitness function requires different primary objective functions that use the image's edge, entropy, grey level co-occurrence matrix (GLCM) contrast, and GLCM energy for increased visual, contrast, and other descriptive information. The results proved that the suggested approach outperforms all-new methods in terms of contrast, edge details, and structural similarity, both subjectively and statistically.
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Luo, Jing, Dan Song, Chunbo Xiu, Shuze Geng, and Tingting Dong. "Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/592928.
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Fingerprint classification is an important indexing scheme to reduce fingerprint matching time for a large database for efficient large-scale identification. The abilities of Curvelet transform capturing directional edges of fingerprint images make the fingerprint suitable to be classified for higher classification accuracy. This paper presents an efficient algorithm for fingerprint classification combining Curvelet transform (CT) and gray-level cooccurrence matrix (GLCM). Firstly, we use fast discrete Curvelet transform warping (FDCT_WARPING) to decompose the original image into five scales Curvelet coefficients and construct the Curvelet filter by Curvelet coefficients relationship at adjacent scales to remove the noise from signals. Secondly, we compute the GLCMs of Curvelet coefficients at the coarsest scale and calculate 16 texture features based on 4 GLCMs. Thirdly, we construct 49 direction features of Curvelet coefficients at the other four scales. Finally, fingerprint classification is accomplished byK-nearest neighbor classifiers. Extensive experiments were performed on 4000 images in the NIST-4 database. The proposed algorithm achieves the classification accuracy of 94.6 percent for the five-class classification problem and 96.8 percent for the four-class classification problem with 1.8 percent rejection, respectively. The experimental results verify that proposed algorithm has higher recognition rate than that of wavelet-based techniques.
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Padayachee, J., M. J. Alport, and W. ID Rae. "Mammographic CAD: Correlation of regions in ipsilateral views - a pilot study." South African Journal of Radiology 13, no. 3 (August 20, 2009): 48. http://dx.doi.org/10.4102/sajr.v13i3.497.
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Background: Radiologists analyse both standard mammographic views of a breast to confirm the presence of abnormalities and reduce false-positives. However, at present no computer-aided diagnosis system uses ipsilateral mammograms to confirm the presence of suspicious features.
Aim: The aim of this study was to develop image-processing algorithms that can be used to match a suspicious feature from one mammographic view to the same feature in another mammographic view of the same breast. This algorithm can be incorporated into a computer-aided diagnosis package to confirm the presence of suspicious features.
Method: The algorithms were applied to 68 matched pairs of cranio-caudal and mediolateral-oblique mammograms. The results of this pilot study take the form of maps of similarity. A novel method of evaluating the similarity maps is presented, using the area under the receiver operating characteristic curve (AUC) and the contrast (C) between the area of the matched region and the background of the similarity map.
Results and Conclusions: The first matching algorithm (using texture measures extracted from a grey-level co-occurrence matrix (GLCM) and a Euclidean distance similarity metric) achieved an average AUC=0.80±0.17 with an average C=0.46±0.26. The second algorithm (using GLCMs and a mutual information similarity metric) achieved an average AUC=0.77±0.25 with an average C=0.50±0.42. The latter algorithm also performed remarkably well with the matching of malignant masses and achieved an average AUC=0.96±0.05 with an average C=0.90±0.21.
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Lee, Heechang, Taeyoung Yoon, Chaeyun Yeo, HyeonYoung Oh, Yebin Ji, Seongwoo Sim, and Daesung Kang. "Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features." Applied Sciences 11, no. 20 (October 12, 2021): 9460. http://dx.doi.org/10.3390/app11209460.
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The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.
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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 (January 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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Wawrzyk-Bochenek, Iga, Mansur Rahnama, Sławomir Wilczyński, and Anna Wawrzyk. "Quantitative Assessment of Hyperpigmentation Changes in Human Skin after Microneedle Mesotherapy Using the Gray-Level Co-Occurrence Matrix (GLCM) Method." Journal of Clinical Medicine 12, no. 16 (August 11, 2023): 5249. http://dx.doi.org/10.3390/jcm12165249.
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Aim: The aim of the study was to quantitatively assess the effectiveness of microneedle mesotherapy in reducing skin discoloration. The results were analyzed using the gray-level co-occurrence matrix (GLCM) method. Material and methods: The skin of the forearm (7 × 7 cm) of 12 women aged 29 to 68 was examined. Microneedle mesotherapy was performed using a dermapen with a preparation containing 12% ascorbic acid. Each of the volunteers underwent a series of four microneedle mesotherapy treatments. The effectiveness of the treatment was quantified using the methods of image analysis and processing. A series of clinical images were taken in cross-polarized light before and after a series of cosmetic procedures. Then, the treated areas were analyzed by determining the parameters of the gray-level co-occurrence matrix (GLCM) algorithm: contrast and homogeneity. Results: During image pre-processing, the volunteers’ clinical images were separated into red (R), green (G) and blue (B) channels. The photos taken after the procedure show an increase in skin brightness compared to the photos taken before the procedure. The average increase in skin brightness after the treatment was 10.6%, the average decrease in GLCM contrast was 10.7%, and the average homogeneity increased by 14.5%. Based on the analysis, the greatest differences in the GLCM contrast were observed during tests performed in the B channel of the RGB scale. With a decrease in GLCM contrast, an increase in postoperative homogeneity of 0.1 was noted, which is 14.5%.
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Oleiwi, Wed Kadhim. "Alzheimer Disease Diagnosis using the K-means, GLCM and K_NN." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 26, no. 2 (December 26, 2017): 57–65. http://dx.doi.org/10.29196/jub.v26i2.474.
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Investigation of medical images have major consequence in the field of treatment.in this work ,MR images have been used to distinguish the normal brain from brain with Alzheimer disease .Texture is an native property of all surfaces it contains important facts about the structural organization of the surfaces and their connections neighboring area. In direction to classify texture must be segmented into a number of section that has the similar properties, for this purpose we used k- means algorithm with GLCM for feature extraction ,finally we used k-nearest neighbor algorithm to distinguish between normal and abnormal brain
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Irawan, Candra, Eko Hari Rachmawanto, Christy Atika Sari, and Raisul Umah Nur. "Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour." Infotekmesin 14, no. 2 (July 29, 2023): 200–207. http://dx.doi.org/10.35970/infotekmesin.v14i2.1827.
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Noni fruit is included in exported food commodities in Indonesia. The size of noni fruit, based on human vision, generally has varied shapes with distinctive textures and various patterns, so that the process of filtering fruit based on color and shape can be done in large quantities. In this study, K-Nearest Neighbor (KNN) has been implemented as a classification algorithm because it has advantages in classifying images and is resistant to noise. Noni imagery is a personal image taken from a noni garden in the morning and undergoes a background subtraction process. The imagery quality improvement technique uses the Hue Saturation Value (HSV) color feature and the Gray Level Co-Occurrence Matrix (GLCM) characteristic feature. KNN accuracy without features is lower than using HSV and GLCM features. From the experimental results, the highest accuracy was obtained using HSV-GLCM at K is 1 and d is 1, namely 95%, while the lowest accuracy was 55% using KNN only at K is 5 and d is 8.
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Aziz, As'ad Shidqy, and Firnanda Al Islama Achyunda Putra. "Effect of Features and Angle on GLCM Feature Extraction on Accuracy for Object Classification." SMARTICS Journal 8, no. 2 (October 31, 2022): 24–30. http://dx.doi.org/10.21067/smartics.v8i2.7627.
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An autonomous car is a vehicle that can guide itself without human intervention. Various types of steeringless vehicles are being developed. The system of the future where computers take over the art of driving. The problem was before it became a concern in autonomous cars to get high safety. Autonomous cars need an early warning system to avoid accidents in front of the car, especially systems that can be used on highway locations. In this paper, we propose a vision-based vehicle detection system for vehicle detection in the form of cars. Our detection algorithm consists of two main components: Extraction of color features using GLCM values, and testing of 6 parameters of GLCM dissimilarity, correlation, homogeneity, contrast, ASM and energy. We use the SVM (Support Vector Machine) algorithm for the classification algorithm. The SVM (Support Vector Machine) classification in previous studies has had quite good results and has a fast computation time. Good accuracy results are found in the ASM feature and using an angle of 450
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., Jyotsna, and Anubhooti Papola. "An Image Encryption Using Chaos Algorithm Based on GLCM and PCA." International Journal of Computer Sciences and Engineering 6, no. 3 (March 30, 2018): 76–81. http://dx.doi.org/10.26438/ijcse/v6i3.7681.
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Choudhary, Jyoti, and Alka Choudhary. "Enhancement in Morphological Mean Filter for Image Denoising Using GLCM Algorithm." International Journal of Computer Theory and Engineering 13, no. 4 (2021): 134–37. http://dx.doi.org/10.7763/ijcte.2021.v13.1302.
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Li, Kexin, Jun Wang, and Dawei Qi. "An Intelligent Warning Method for Diagnosing Underwater Structural Damage." Algorithms 12, no. 9 (August 30, 2019): 183. http://dx.doi.org/10.3390/a12090183.
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A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations.
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Evandari, Kristhina, M. Arief Soeleman, and Ricardus Anggi Pramunendar. "BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 2 (March 26, 2023): 293–301. http://dx.doi.org/10.29207/resti.v7i2.4743.
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Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.
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Jayandhi, G., Dr R.Dhaya, and Dr R.Kanthavel. "Multi Intelligent Fuzzy Integration (Mifi) Support Vector Machines for the Mammogram Classification." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 251. http://dx.doi.org/10.14419/ijet.v7i3.34.18978.
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Breast cancer is the most important problem across the globe in which the 80% of the women are suffering without knowing the causes and effects of the cancer cells. Mammogram Image is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Micro calcification in mammogram images. The new technique MIFI-SVM has been proposed which integrates the GLCM features along with the Fuzzy Support Vector Machines. ROI Segmentation using Saliency maps has been used for the proposed algorithm and feature is extracted using GLCM and fed to Fuzzy Support Vector Machines The MIAS datasets has been used for testing the proposed algorithm and accuracy, sensitivity has been measured which proves to be better when compared to other Multi-level SVM’s, C-SVM and Neural Networks.
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Aggarwal, Ashwani Kumar. "Learning Texture Features from GLCM for Classification of Brain Tumor MRI Images using Random Forest Classifier." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 18 (April 19, 2022): 60–63. http://dx.doi.org/10.37394/232014.2022.18.8.
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In computer vision, image feature extraction methods are used to extract features so that the features are learnt for classification tasks. In biomedical images, the choice of a particular feature extractor from a diverse range of feature extractors is not only subjective but also it is time consuming to choose the optimum parameters for a particular feature extraction algorithm. In this paper, the focus is on the Grey-level co-occurrence matrix (GLCM) feature extractor for classification of brain tumor MRI images using random forest classifier. A dataset of brain MRI images (245 images) consisting of two classes viz. images with tumor (154 images) and images without tumor (91 images) has been used to assess the performance of GLCM features on random forest classifier in terms of accuracy, true positive rate, true negative rate, false positive rate, false negative rate derived from the confusion matrix. The results show that by using optimum parameters, the GLCM feature extracts significant texture component in brain MRI images for promising accuracy and other performance metrics.
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Liu, Xiaoming, Ke Xu, Peng Zhou, and Huajie Liu. "Feature Extraction with Discrete Non-Separable Shearlet Transform and Its Application to Surface Inspection of Continuous Casting Slabs." Applied Sciences 9, no. 21 (November 1, 2019): 4668. http://dx.doi.org/10.3390/app9214668.
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A new feature extraction technique called DNST-GLCM-KSR (discrete non-separable shearlet transform-gray-level co-occurrence matrix-kernel spectral regression) is presented according to the direction and texture information of surface defects of continuous casting slabs with complex backgrounds. The discrete non-separable shearlet transform (DNST) is a new multi-scale geometric analysis method that provides excellent localization properties and directional selectivity. The gray-level co-occurrence matrix (GLCM) is a texture feature extraction technology. We combine DNST features with GLCM features to characterize defects of the continuous casting slabs. Since the combination feature is high-dimensional and redundant, kernel spectral regression (KSR) algorithm was used to remove redundancy. The low-dimension features obtained and labels data were inputted to a support vector machine (SVM) for classification. The samples collected from the continuous casting slab industrial production line—including cracks, scales, lighting variation, and slag marks—and the proposed scheme were tested. The test results show that the scheme can improve the classification accuracy to 96.37%, which provides a new approach for surface defect recognition of continuous casting slabs.
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Lu, Yi, Chenyang Huang, Jia Wang, and Peng Shang. "An Improved Quantitative Analysis Method for Plant Cortical Microtubules." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/637183.
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The arrangement of plant cortical microtubules can reflect the physiological state of cells. However, little attention has been paid to the image quantitative analysis of plant cortical microtubules so far. In this paper, Bidimensional Empirical Mode Decomposition (BEMD) algorithm was applied in the image preprocessing of the original microtubule image. And then Intrinsic Mode Function 1 (IMF1) image obtained by decomposition was selected to do the texture analysis based on Grey-Level Cooccurrence Matrix (GLCM) algorithm. Meanwhile, in order to further verify its reliability, the proposed texture analysis method was utilized to distinguish different images of Arabidopsis microtubules. The results showed that the effect of BEMD algorithm on edge preserving accompanied with noise reduction was positive, and the geometrical characteristic of the texture was obvious. Four texture parameters extracted by GLCM perfectly reflected the different arrangements between the two images of cortical microtubules. In summary, the results indicate that this method is feasible and effective for the image quantitative analysis of plant cortical microtubules. It not only provides a new quantitative approach for the comprehensive study of the role played by microtubules in cell life activities but also supplies references for other similar studies.
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Fati, Suliman Mohamed, Ebrahim Mohammed Senan, and Yasir Javed. "Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches." Diagnostics 12, no. 8 (August 5, 2022): 1899. http://dx.doi.org/10.3390/diagnostics12081899.
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Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.
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Xing, Zhikai, and Heming Jia. "Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm." IEEE Access 7 (2019): 37672–90. http://dx.doi.org/10.1109/access.2019.2904511.
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Sinaga, Daurat, Feri Agustina, Noor Ageng Setiyanto, Suprayogi Suprayogi, and Cahaya Jatmoko. "Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm." Journal of Applied Intelligent System 6, no. 2 (December 6, 2021): 111–19. http://dx.doi.org/10.33633/jais.v6i2.4627.
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Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.
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Zhu, Zheng Tao, Gang Yang, and Bo Zhang. "Image Surface Defects Extraction Technology Based on Watershed Segmentation and Texture Analysis." Applied Mechanics and Materials 236-237 (November 2012): 603–8. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.603.
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Images of glass product that are captured under poor light usually make defected regions covered by dark pixels because of refraction and scattering brought about by defects on glass. On the other hand, image texture makes it unlikely to effectively identify defects accompanied by some texture by using commonly-used image segmentation and defects extraction algorithms. Watershed segmentation algorithm is proposed in this paper to extract catchment basin where defects characteristics in each image region will be calculated with the twofold application of gray level co-occurrence matrix(GLCM) and parameters characteristics . Defects will be finally identified using shape operator.
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Abuhussein, Mohammed, and Aaron Robinson. "Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures." Journal of Imaging 8, no. 10 (September 30, 2022): 266. http://dx.doi.org/10.3390/jimaging8100266.
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The benefits of autonomous image segmentation are readily apparent in many applications and garners interest from stakeholders in many fields. The wide range of benefits encompass applications ranging from medical diagnosis, where the shape of the grouped pixels increases diagnosis accuracy, to autonomous vehicles where the grouping of pixels defines roadways, traffic signs, other vehicles, etc. It even proves beneficial in many phases of machine learning, where the resulting segmentation can be used as inputs to the network or as labels for training. The majority of the available image segmentation algorithmic development and results focus on visible image modalities. Therefore, in this treatment, the authors present the results of a study designed to identify and improve current semantic methods for infrared scene segmentation. Specifically, the goal is to propose a novel approach to provide tile-based segmentation of occlusion clouds in Long Wave Infrared images. This work complements the collection of well-known semantic segmentation algorithms applicable to thermal images but requires a vast dataset to provide accurate performance. We document performance in applications where the distinction between dust cloud tiles and clear tiles enables conditional processing. Therefore, the authors propose a Gray Level Co-Occurrence Matrix (GLCM) based method for infrared image segmentation. The main idea of our approach is that GLCM features are extracted from local tiles in the image and used to train a binary classifier to provide indication of tile occlusions. Our method introduces a new texture analysis scheme that is more suitable for image segmentation than the solitary Gabor segmentation or Markov Random Field (MRF) scheme. Our experimental results show that our algorithm performs well in terms of accuracy and a better inter-region homogeneity than the pixel-based infrared image segmentation algorithms.
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Anand, L., Kantilal Pitambar Rane, Laxmi A. Bewoor, Jyoti L. Bangare, Jyoti Surve, Mutkule Prasad Raghunath, K. Sakthidasan Sankaran, and Bernard Osei. "Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images." Computational Intelligence and Neuroscience 2022 (August 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/7797094.
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The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.
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Zhang, Chunlong, Kunlin Zou, and Yue Pan. "A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning." Agronomy 10, no. 7 (July 6, 2020): 972. http://dx.doi.org/10.3390/agronomy10070972.
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Apples are one of the most kind of important fruit in the world. China has been the largest apple producing country. Yield estimating, robot harvesting, precise spraying are important processes for precise planting apples. Image segmentation is an important step in machine vision systems for precision apple planting. In this paper, an apple fruit segmentation algorithm applied in the orchard was studied. The effect of many color features in classifying apple fruit pixels from other pixels was evaluated. Three color features were selected. This color features could effectively distinguish the apple fruit pixels from other pixels. The GLCM (Grey-Level Co-occurrence Matrix) was used to extract texture features. The best distance and orientation parameters for GLCM were found. Nine machine learning algorithms had been used to develop pixel classifiers. The classifier was trained with 100 pixels and tested with 100 pixels. The accuracy of the classifier based on Random Forest reached 0.94. One hundred images of an apple orchard were artificially labeled with apple fruit pixels and other pixels. At the same time, a classifier was used to segment these images. Regression analysis was performed on the results of artificial labeling and classifier classification. The average values of Af (segmentation error), FPR (false positive rate) and FNR (false negative rate) were 0.07, 0.13 and 0.15, respectively. This result showed that this algorithm could segment apple fruit in orchard images effectively. It could provide a reference for precise apple planting management.
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Sukenda, Sukenda, Ari Purno Wahyu, Benny Yustim, Sunjana Sunjana, and Yan Puspitarani. "IMAGE PROCESSING BASED TILAPIA SORTATION SYSTEM USING NA." Jurnal Ilmiah Teknologi Infomasi Terapan 7, no. 1 (January 1, 2021): 83–88. http://dx.doi.org/10.33197/jitter.vol7.iss1.2020.459.
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Tilapia has a value of export quality and is imported from America and Europe, tilapia is cultivated in freshwater, the largest tilapia producing areas are Java and Bali for the export market in the Middle East, value fish with a size of 250 grams / head (4 fish / kg ) in their intact form is in great demand. According to news circulating, fish of this size in the Middle East are ordered to meet the consumption of workers from Asia. the fish classification process is a very difficult process to find the quality value of the fish to be sold to meet export quality. Fish classification techniques can use the GLCM technique (Gray Level Oc-Currance Matrix) classification using images of fish critters with the GLCM method.The fish image data is analyzed based on the value of Attribute, Energy, Homogenity, Correlation, Contrash, from the attribute the density data matrix is ??generated for each. Fish image data and displayed in the form of a histogram, the data from the GLCM results are then classified with the Naive Bayes algorithm, from the results of the classification of data taken from 3 types of tilapia from the types of gift, Red, and Blue.
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Muzakir, Ari, and Usman Ependi. "Model for Identification and Prediction of Leaf Patterns: Preliminary Study for Improvement." Scientific Journal of Informatics 8, no. 2 (November 30, 2021): 244–50. http://dx.doi.org/10.15294/sji.v8i2.30024.
Full textAbstract:
Purpose: Many studies have conducted studies related to automation for image-based plant species identification recently. Types of plants, in general, can be identified by looking at the shape of the leaves, colors, stems, flowers, and others. Not everyone can immediately recognize the types of plants scattered around the environment. In Indonesia, herbal plants thrive and are abundantly found and used as a concoction of traditional medicine known for its medicinal properties from generation to generation. In the current Z-generation era, children lack an understanding of the types of plants that benefit life. This study identifies and predicts the pattern of the leaf shape of herbal plants. Methods: The dataset used in this study used 15 types of herbal plants with 30 leaf data for each plant to obtain 450 data used. The extraction process uses the GLCM algorithm, and classification uses the K-NN algorithm. Result: The results carried out through the testing process in this study showed that the accuracy rate of the leaf pattern prediction process was 74% of the total 15 types of plants used. Value: Process of identifying and predicting leaf patterns of herbal plants can be applied using the K-NN classification algorithm combined with GLCM with the level of accuracy obtained.