A Method to Differentiate Mild XXXX and Alzheimer in MR Images using Eigen Value Descriptors.

@entity1 @entity303 @entity73 @entity1283 @entity74 @entity195 @entity10629
UNASSIGNED: Automated analysis and differentiation of mild @entity73 and @entity195 using MR images is clinically significant in @entity74 . @entity195 ( @entity195 ) is a fatal and common form of @entity303 that progressively affects the @entity1 . Shape descriptors could better differentiate the morphological alterations of brain structures and aid in the development of prospective disease modifying therapies. @entity1283 is considered as a significant biomarker in the @entity195 diagnosis. In this work, a method has been proposed to differentiate MCI from the healthy normal and @entity195 subjects using Laplace-Beltrami (LB) eigen value shape descriptors. Prior to this, Reaction Diffusion (RD) level set is used to segment the ventricles in MR images and the results are validated against the @entity10629 ( @entity10629 ). LB eigen values are infinite series of spectrum that describes the intrinsic geometry of objects. Most significant LB shape descriptors are identified and their performance is analysed using linear Support Vector Machine (SVM) classifier. Results show that, the RD level set is able to segment the ventricles. The segmented ventricles are found to have high correlation with @entity10629 . The eigen values in the LB spectrum could show distinction in the feature space better than the geometric features. High accuracy is observed in the classification results of linear SVM. The proposed automated system is able to distinctly separate the MCI from normal and @entity195 subjects. Thus this pipeline of work seems to be clinically significant in the automated analysis of dementic subjects.
