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An SVM Based Automatic Segmentation Method for Brain Magnetic Resonance Image Series

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5 Author(s)
Bofeng Zhang ; Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China ; Wenhao Zhu ; Hui Zhu ; Anping Song
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To segment magnetic resonance image series is an interdisciplinary topic that involves both medical and computer science. It is one of the most important steps for medical diagnosis and quantitative analysis. This paper proposes an automatic segmentation method based on support vector machine (SVM). Feature vectors are generated according to both grayscale value and texture pattern of MR brain images. To speed up, some results are acquired directly from the segmentation model trained in adjacent layers. Further more, morphological image processing is introduced to refine the image contour. The experiment shows that our method can achieve good segmentation results in a fast way.

Published in:

Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2010 7th International Conference on

Date of Conference:

26-29 Oct. 2010