MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

作者：Shen S^*; Sandham W; Granat M; Sterr A

来源：IEEE Transactions on Information Technology in Biomedicine, 2005, 9(3): 459-467.

DOI：10.1109/TITB.2005.847500

• improved fuzzy c-means clustering (IFCM)
• magnetic resonance imaging (MRI)
• neighborhood attraction
• segmentation

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.

• 出版日期2005-9