Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
عنوان مقاله: Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
شناسه ملی مقاله: JR_IJIEPR-23-4_001
منتشر شده در شماره 4 دوره 23 فصل در سال 1391
شناسه ملی مقاله: JR_IJIEPR-23-4_001
منتشر شده در شماره 4 دوره 23 فصل در سال 1391
مشخصات نویسندگان مقاله:
M.H. Fazel Zarandi - Professor of Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
M. Zarinbal - Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
خلاصه مقاله:
M.H. Fazel Zarandi - Professor of Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
M. Zarinbal - Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-2 fuzzy clustering is the most preferred method. In recent years, neurology and neuroscience have been significantly advanced by imaging tools, which typically involve vast amount of data and many uncertainties. Therefore, Type-2 fuzzy clustering methods could process these images more efficient and could provide better performance. The focus of this paper is to segment the brain Magnetic Resonance Imaging (MRI) in to essential clusters based on Type-2 Possibilistic C-Mean (PCM) method. The results show that using Type-2 PCM method provides better results.
کلمات کلیدی: Brain Tumors Diagnosis, Image segmentation, Type-2 Fuzzy Logic, Type-2 PCM
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/281402/