Improvement of diagnosis in patients with Mild Cognitive Impairment (MCI) would help in the early diagnosis of Alzheimer's Disease (AD), since MCI was known as the transition state from normal aging to AD. We developed a novel feature extraction algorithm, which was based on the disease clinical-pathological understanding and combined with the spatial information between disease affected pattern and its surrounding regions. This novel feature set demonstrated improved diagnostic performance, compared to the conventional feature set, especially for the MCI patients. In addition, the identified disease affected pattern corresponded with the postmortem pathology of amyloid deposition in AD patients.
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