Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation

摘要

Precise brain tissue segmentation and analysis in the presence of intensity non-uniformity (INU) and noise is the challenging task due to intensity overlaps between data pixels within the image. The clustering method is commonly used to variety of applications for grouping similar data items. Specifically, the fuzzy C-means (FCM) clustering method is extensively used in many real world and research applications. In this paper, the rough fuzzy region based bounded support fuzzy C-means (RFRBSFCM) clustering method is proposed for brain MR image INU estimation and correction, and segmentation. The rough fuzzy regions are estimated based on similarity distance vector and it is determined from both local and global spatial information. In addition, the proposed algorithm incorporates bounded support vector for estimating weighted image. The objective function of proposed algorithm is minimized for segmenting different tissues in brain MR image. The RFRBSFCM algorithm is tested with recent FCM clustering techniques using simulated T1 and T2-weighted brain MR images from public BrainWeb dataset. The quantitative results confirm that the proposed algorithm achieves superior performance than other recent state-of-the-art methods.