A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease

摘要

Identification of dominant imaging biomarkers is important for early detection of Alzheimer's disease (AD) and to improve diagnostic accuracy. This work proposes a novel automatic computer aided diagnosis (CAD) system working on region selection framework. Voxel based morphometry and tissue segmentation is performed to get gray matter (GM) images. These pre-processed images are anatomized to get 116 regions of brain using a standard automated anatomical labeling atlas. The proposed region selection algorithm identifies the most relevant brain regions out of 116 regions to discriminate AD and healthy control (HC) subjects. Volumetric features (standard deviation, skewness, kurtosis, energy, and shannon entropy) are extracted and random feature selection is performed to get the most discriminating regions to classify AD from HC. Supervised classification algorithms are used to explore and validate the proposed methodology. Experimental results indicate that the performance of the proposed system competes well with the state-of-the-art techniques.