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44 articles for “texture analysis”
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Research ArticleOpen accessPhrase in the titleExtracting the Potential Features of Digital Panoramic Radiograph Images by Combining Radio Morphometry Index, Texture Analysis, and Morphological FeaturesEnny Itje Sela, SutarmanOsteoporosis is a type of disease that is not easily detected visually. It contributes to bone fracture and so early diagnosis is particularly important to prevent bone fracture. An integrated approach for extraction of cortical and trabecular bone on the digital panoramic radiograph (DPR) images was proposed to screen osteoporosis. We performed radio morphometry index (RMI), texture analysis, and morphology … …Journal of Computer Science, Vol. 14, Issue 2 · 7 Dec 2017 · 8,026 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleLand Cover Change Detection Using Texture AnalysisXiaofeng Wu, Fen Yang, Roly LishmanProblem statement: It is an important task to detect land cover changes from remotely sensed data for environmental monitoring. Although there are some applications of visual textures to the land use, they are limited to a few land cover categories with the application of one texture measure. Since land cover types are complex and often the integration of various objects, … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 8,198 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleOptimized Local Ternary Patterns: A New Texture Model with Set of Optimal Patterns for Texture AnalysisG. Madasamy Raja, V. SadasivamTexture analysis is one of the important as well as useful tasks in image processing applications. Many texture models have been developed over the past few years and Local Binary Patterns (LBP) is one of the simple and efficient approach among them. A number of extensions to the LBP method have been also presented but the problem remains challenging in … …Journal of Computer Science, Vol. 9, Issue 1 · 22 Jan 2013 · 6,951 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePERFORMANCE ANALYSIS OF GRAY LEVEL CO-OCCURRENCE MATRIX TEXTURE FEATURES FOR GLAUCOMA DIAGNOSISSakthivel Karthikeyan, N. RengarajanGlaucoma is a multifactorial optic neuropathy disease characterized by elevated Intra Ocular Pressure (IOP). As the visual loss caused by the disease is irreversible, early detection is essential. Fundus images are used as input and it is preprocessed using histogram equalization. First order features from histogram and second order features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the … …American Journal of Applied Sciences, Vol. 11, Issue 2 · 24 Dec 2013 · 7,326 accessesView PDFCite
Research ArticleOpen accessIn the keywordsApplication of Morphological Component Analysis to Optical Image FusionGeorges Laussane Loum, Atiampo Kodjo Armand, Pandry Koffi Ghislain, Souleymane OumtanagaThe image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth … …American Journal of Applied Sciences, Vol. 14, Issue 8 · 11 Aug 2017 · 7,729 accessesView PDFCite
Research ArticleOpen accessIn the keywordsNew and Efficient Features for Skin Lesion Classification based on SkeletonizationYoussef Filali, Hasnae El Khoukhi, My Abdelouahed Sabri et al.This paper presents a new approach to detect and classify skin lesions for melanoma diagnosis with high accuracy. Skin lesion detection is based on an image decomposition into two components using the Partial Differential Equation (PDE). The first component that sufficiently preserves the contour is thus exploited to have an adequate segmentation of image lesion while the second component provides … …Journal of Computer Science, Vol. 15, Issue 9 · 2 Sep 2019 · 8,028 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGray-Level Co-occurrence Matrix Bone Fracture DetectionHum Yan Chai, Lai Khin Wee, Tan Tian Swee et al.Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the … …American Journal of Applied Sciences, Vol. 8, Issue 1 · 31 Jan 2011 · 8,968 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPsoriasis Detection Using Skin Color and Texture FeaturesNidhal K. El Abbadi, Nizar Saadi Dahir, Muhsin A. AL-Dhalimi, Hind RestomProblem statement: In this study a skin disease diagnosis system was developed and tested. The system was used for diagnosis of psoriases skin disease. Approach: Present study relied on both skin color and texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin diseases. We used feed forward neural networks to classify … …Journal of Computer Science, Vol. 6, Issue 6 · 30 Jun 2010 · 8,290 accessesView PDFCite
Research ArticleOpen accessIn the keywordsIndexing Medical Images Using the Wavelet Transform: Application to Retinal ImagesSouad Meziane Tani, Abdelhafid BessaidContent-Based Image Retrieval Systems (CBIR) provide effective support to the doctor to find relevant images using their visual content. Current medical systems are unable to provide accurate and precise results. To solve this problem, effective indexing algorithms are required to extract the contents of the images. In this study, the method of indexing image from the energy coefficients of the … …Current Research in Bioinformatics, Vol. 2, Issue 2 · 1 Jul 2014 · 6,691 accessesView PDFCite
Research ArticleOpen accessIn the abstractDual-Branch Shape Texture Learning With Pyramid Residual and Sobel Edge Layers for Rice Variety ClassificationM Pradeep, M SiddappaAutomated rice variety classification categorizes rice types based on features, such as grain texture, shape, color, and size. Traditional handcrafted feature-based models fail at rice classification due to lighting variations, background noise, and environmental conditions, leading to overlapping visual characteristics and frequent misclassification. To address these challenges, this research proposes a novel Dual-Branch Convolutional Neural Network with Pyramid Residual Units … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 627 accessesView PDFCite