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An Automatic FCM-Based Method for Tissue Classification Application to MRI of Thigh

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5 Author(s)
Han Kang ; LAMIH, Univ. de Valenciennes, Valenciennes ; Antonio Pinti ; Laurent Vermeiren ; Abdelmalik Taleb-Ahmed
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Fuzzy C-means (FCM) has been frequently used to image segmentation in order to separate objects. The most used segmentation attribute is grey level of pixels. Nevertheless, this method can not identify complex image objects because grey level can not take into account all visual information. This paper describes a modified FCM method for tissue classification using retrospective operation of partition tree with expert knowledge. This method is applied to 26 MRI (Magnetic Resonance Imaging) images of thigh for localizing four main anatomical tissues: muscle, adipose tissue, cortical bone, and spongy bone. A test dataset of 6500 representative points has been created by an expert. Using our method, we obtain a classification rate of 95.73% in the test dataset, which largely improved the classification results obtained from existing methods.

Published in:

2007 1st International Conference on Bioinformatics and Biomedical Engineering

Date of Conference:

6-8 July 2007