Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers

Results show that, RD level set is able to segment CC in normal and AD images with high percentage of similarity with GT. The extracted LB eigen values are found to show high difference in the mean values between normal and AD subjects with high statistical significance. The LB eigen modes λ2, λ7 and λ8 are identified as prominent features by IG based ranking. KNN is able to give maximum classification accuracy of 93.37% compared to linear SVM and NB classifiers. This value is observed to be high than the results obtained using geometric features. The proposed CAD system focuses solely on the geometric variations of CC extracted using LB eigen value spectrum. The extraction of eigen modes in the LB spectrum is easy to compute, does not involve too many parameters and less time consuming. Thus this CAD study seems to be clinically significant in the shape investigation of brain structures for AD diagnosis.