Conference Paper

Atlas-based segmentation of brain MR images using least square support vector machines

Dept. of Electr. & Electron. Eng., Shiraz Univ. of Technol., Shiraz, Iran
DOI: 10.1109/IPTA.2010.5586779 Conference: Image Processing Theory Tools and Applications (IPTA), 2010 2nd International Conference on
Source: IEEE Xplore

ABSTRACT This study presents an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images. In this paper, support vector machine (SVM) based classifier, as a new and powerful kind of supervised machine learning with high generalization characteristics, is employed. Here, least-square SVM (LS-SVM) in conjunction with brain probabilistic atlas as a priori information is applied to obtain class probabilities for three tissues of cerebrospinal fluid (CSF), white matter (WM) and grey matter (GM). The entire process of brain segmentation is performed in an iterative procedure, so that the probabilistic maps of brain tissues will be updated at any iteration. The quantitative and qualitative results indicate excellent performance of the applied method.

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    ABSTRACT: Magnetic Resonant Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis. In this paper, we describe a method for segmentation of White matter and Gray matter from real MR images using a LM-k-means technique. After preprocessing, a simple unsupervised clustering system like k-means is taken and made into a supervised system by using Levenberg-Marquardt optimization technique. It was inferred that a k-means system does not arrive on its own at the means which will give a good segmentation. Hence the LM algorithm trains it for that purpose. The results are compared with that of a k-means system and they show a considerable improvement with a much higher precision.
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