MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. - Shan Shen
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed.
Keywords: MR image-based classification system; MRI fuzzy segmentation; Brain tissue segmentation; Brain tumors; Human tissue visualization; Image segmentation; Magnetic resonance image analysis; Neighborhood attraction; Neural-network model; Neural-network optimization; Traditional fuzzy c-means clustering algorithm