Abstract: Now-a-days, almost all biomedical science use magnetic resonance image for diagnosis. Segmentation plays a vital role in such biomedical science.. Hence, there is a need to improved result by proposed method as suggested by authors.. Various approaches have been reported in literature for segmentation of brain, where main objective is separating the pixels associated with different types of tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). This paper introduces an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images by using support vector machine (SVM) based classifier. A new and powerful kind of supervised machine learning with high generalization characteristics, is employedby SVM. Another classifier such as neural network plays an important role in biomedical science for improved result for health care system. This paper will analyseSegmentation algorithm and Proved comparative study.

• Publication Date: 24-Feb-2013
• DOI: 10.15224/978-981-07-5461-7-34
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Abstract: This paper is focused on gait analysis methodologies for clinical use. For the purpose of explanation instrumented measurement of the movement patterns that make up walking and the associated interpretation of the same are considered. In this paper, Brand\'s four reasons for clinical tests, gait cycle and analysis of the varying characteristics are considered as a basis for determining what methodologies are required for gait analysis in a clinical rehabilitation context. It gives overall comparative study of various techniques used for gait analysis and rehabilitation along with the suggested improvements.

• Publication Date: 24-Feb-2013
• DOI: 10.15224/978-981-07-5461-7-35
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• Downloads: 0