Abstract
Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.
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Rahim, M.S.M., Saba, T., Nayer, F. et al. 3D Texture Features Mining for MRI Brain Tumor Identification. 3D Res 5, 3 (2014). https://doi.org/10.1007/s13319-013-0003-2
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DOI: https://doi.org/10.1007/s13319-013-0003-2