New Computer Assisted Diagnostic to Detect Alzheimer Disease

Abstract

We describe a new Computer Assisted Diagnosis (CAD) to automatically detect Alzheimer Patients (AD), Mild Cognitive Impairment (MCI) and elderly Controls, based on the segmentation and classification of the Hippocampus (H) and Corpus Calosum (CC) from Magnetic Resonance Images (MRI). For the segmentation we used a new method based on a deformable model to extract the area wishes, and then we computed the geometric and texture features. For the classification we proposed a new supervised method. We evaluated the accuracy of our method in a group of 25 patients with AD (age±standard-deviation (SD) =70±6 years), 25 patients with MCI (age±SD=65±8 years) and 25 elderly healthy controls (age±SD=60±8 years). For the AD patients we found an accuracy of the classification of 92%, for the MCI we found 88% and for the elderly patients we found 96%. Overall, we found our method to be 92% accurate. Our method can be a useful tool for diagnosing Alzheimer’s Disease in any of these Steps.

Keywords

• Computer Assisted Diagnosis (CAD), Alzheimer disease (AD), Mild Cognitive Impairment (MCI), Corpus Calosum (CC), Hippocampus (H), Magnetic Resonance Imaging (MRI), Standard Deviation (SD)