SVM Based Classification of Neurodegenerative Diseases for Salient Brain Patterns
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 11
Abstract
The identification defects in the MRI brain images can save numerous lives. A method to implement the kernel function for feature extraction to identify the neurodegenerative Alzheimer disease in Brain Image is proposed. The input Brain image has converted into gray image and preprocessed and saliency map image is obtained from the preprocessed image. The saliency map obtained gives the intensity related information from the images. After getting the saliency map image we have to normalize the saliency map and applying the kernel fusion to the normalize image to extract the feature of the image. The normalization process refines the images pixels to certain extend and the exact information representing the different portions separately is obtained. The feature extraction process reduces the dimensionality of the image data to make the process more optimized and simple. Finally by using the SVM classifier is fed with features such as intensity, textural and statistical information, binary tissue segmentations or cortical thickness estimations. Overall proposed algorithm used to decrease the computational time and the presence of irrelevant and noisy features. The salient regions found with the proposed approach as systematically relevant for discrimination of AD patients this results completely coherent to what has been reported by clinical studies of AD.
Authors and Affiliations
S. Subashini, Mr. S. Sakkaravarthi
Keywords
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