title: A Method to Differentiate Mild Cognitive Impairment and Alzheimer in MR Images using Eigen Value Descriptors.
abstract: Automated analysis and differentiation of mild cognitive impairment and Alzheimer's condition using MR images is clinically significant in dementic disorder. Alzheimer's Disease (AD) is a fatal and common form of dementia that progressively affects the patients. Shape descriptors could better differentiate the morphological alterations of brain structures and aid in the development of prospective disease modifying therapies. Ventricle enlargement is considered as a significant biomarker in the AD diagnosis. In this work, a method has been proposed to differentiate MCI from the healthy normal and AD 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 Ground Truth (GT). 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 GT. 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 AD subjects. Thus this pipeline of work seems to be clinically significant in the automated analysis of dementic subjects.
authors.affiliation: Indian Institute of Technology Madras, Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Chennai, India. anandhmurali@gmail.com.|CEG Campus, Department of Electronics and Communication Engineering, Anna University, Chennai, India. anandhmurali@gmail.com.
authors.forename: K R
authors.initials: KR
authors.lastname: Anandh
authors.affiliation: CEG Campus, Department of Electronics and Communication Engineering, Anna University, Chennai, India.
authors.forename: C M
authors.initials: CM
authors.lastname: Sujatha
authors.affiliation: Indian Institute of Technology Madras, Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Chennai, India.
authors.forename: S
authors.initials: S
authors.lastname: Ramakrishnan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D015415:Biomarkers; D001921:Brain / Q000503:physiopathology*; D060825:Cognitive Dysfunction / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D015415:Biomarkers
keywords: Alzheimer’s disease; Automated analysis; Laplace-Beltrami shape descriptors; MR imaging; Ventricle segmentation