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Abstract #3244

An Automatic Classification of Alzheimer’s Disease Based on Structural MRI Data Compared with Voxel-Based Morphometry Method

Xiangzhu Zeng1, Huishu Yuan1, Yan Liu2, Ling Wang3, Ying Liu1, Zheng Wang1, and Lizhi Xie4

1Department of Radiology, Peking University Third Hospital, Beijing, China, 2University of Chinese Academy of Sciences, Beijing, China, 3University of Electronic Science and Technology of China, Chengdu, China, 4MR Research, GE Healthcare, Beijing, China

In this study, a compartmental sparse feature selection method was used with feature parameter identified, and compared with classical voxel-based morphometry method (VBM) for classification of Alzheimer ’s disease (AD) from the healthy subjects. Our method had high classification accuracy for AD diagnosis and a strong linear correlation between the extracted feature parameter and volume of hippocampus obtained by VBM. The feature parameter of hippocampus had a higher linear correlation with mini-mental state examination (MMSE) score than volume of hippocampus with MMSE. Hence, compartmental sparse feature selection is an effective computer-aided diagnosis method to help clinician identify AD.

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