Rough-Fuzzy Clustering Algorithm for Segmentation of Brain MR Images

• Published in: Rough Fuzzy Image Analysis pp. 23 - 43
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2010; Taylor & Francis Group
• Subjects: Applied mathematics; Automatic control engineering; Image processing; Inverse Difference Moment; Fuzzy Sets; Cerebro Spinal Fluid; Pixel Classification; Brain MR Image; Haralick’s Features; Sum Entropy; Rough Sets; Data Set; Dunn Index; Cluster Prototypes; MR Image; Execution Time; Gray Levels; Gray Level Co-occurrence Matrix; Segmented Versions; Db Index; Medical Image Segmentation; Membership Function; Initial Centroids; Rough Set Theory; Set Iteration Counter; Cluster Βi; Highest Memberships
• ISBN: 9781439803295; 1439803293
• DOI: 10.1201/9781439803301-6

Segmentation is a process of partitioning an image space into some non-overlapping meaningful homogeneous regions. The success of an image analysis system depends on the quality of segmentation (Rosenfeld and Kak, 1982). A segmentation method is supposed to find those sets that correspond to distinct anatomical structures or regions of interest in the image. In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary stage. However, medical image segmentation is a complex and challenging task due to intrinsic nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues, in order to prescribe appropriate therapy (Suetens, 2002).