Quasi-conformal statistical shape analysis of hippocampal surfaces for Alzheimerâ??s disease analysis
3rd World Congress on Pharmacology
August 08-10, 2016 Birmingham, UK

Hangfan Li

Georgia Institute of Technology, USA

Posters & Accepted Abstracts: Clin Exp Pharmacol

Abstract:

Alzheimer�??s Disease (AD) is a no-cure disease that has been creating troubles to scientists for many years. Analyzing the disease has become an important but challenging research topic. The shape analysis of the sub-cortical structure of AD patients has been commonly used to understand this disease. In this paper, we assess the feasibility of using shape information on the hippocampal (HP) surfaces to detect some sub-structural changes in AD patients. We propose a quasi-conformal statistical shape analysis model, which allows us to study local regional geometric changes of the HPs amongst normal control (NC) and AD groups. A shape index defined by the quasi-conformality and surface curvatures is used to characterize region-specific shape variations of the HP surfaces. Feature vectors can be extracted for each HPs, with which a classification model can be built using machine learning methods to classify HPs into NC and AD subjects. Experiments have been carried out on 99 normal controls and 41 patients with AD. Results demonstrate the proposed quasi-conformal based model is effective for classifying HPs into NC and AD groups with high classification accuracy (with highest overall classification accuracy reaches up to 87.86% in a leave-one-out experiment).

Biography :

Email: frankliforever430@gmail.com