MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization
Shen, Shan, Sandham, William, Granat, Malcolm and Sterr, Annette (2005) MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization IEEE Transactions on Information Technology in Biomedicine, 9 (3). pp. 459-467.
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.
|Divisions :||Faculty of Health and Medical Sciences > School of Psychology|
|Date :||1 September 2005|
|Identification Number :||https://doi.org/10.1109/TITB.2005.847500|
|Additional Information :||In IEEE Transactions on Information Technology in Biomedicine, , 9, 459- 467.© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Depositing User :||Mr Adam Field|
|Date Deposited :||27 May 2010 14:43|
|Last Modified :||23 Sep 2013 18:34|
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