T Matsuura, S Kobashi, Y Hata
KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2 69 436-440 2001年 査読有り
Image segmentation is one of the fundamental techniques to develop a computer-aided diagnosis (CAD) system in the medical field. This paper first introduces rough sets into image segmentation method. In this method, attribute values of each pixel of an image of interest are given by using K-means clustering, and the attribute values divide the image into many regions. By applying value reduct, which is one of the typical concepts of rough sets, to the attribute values, dissimilarities between regions are calculated. Final clustering result is obtained by merging similar regions. To evaluate the performance of the proposed image segmentation method, it was applied to an artificial generated image, and a human brain Magnetic Resonance (MR) image. The results were also compared with convention K-means clustering.