Segmentation and quantification for Alzheimer's disease (AD) - A preliminary study

Alzheimer's is a progressive brain disease and is clinically characterized by cognitive symptoms that, in combination with behavioral disturbances, significantly interfere with activities of daily living. The purpose of this study is to investigate the possibility of developing volumetric measures of the structural damage and atrophy of brain derived from multiprotocol MR imaging. Our approach first applies intensity inhomogeneity correction and intensity standardization to PD and T2 weighted MR images to create base images for quantitative image analysis. Then, vectorial scale-based fuzzy connectedness segmentation (VSFCS) and morphological operations are applied to the base images to extract masks of cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM), and further to create a clean and accurate intracranial (IC) mask. After separating CSF from brain parenchyma (BP), VSFCS is applied to BP (PD and T2) images to generate pure GM and WM masks, and then subtracting these pure from the BP mask to detect AD lesions. This method was applied to a set of conventional PD and T2 weighted MR images that were obtained from 5 patients with probable AD and 5 healthy normal control subjects. The segmented images of individual brain tissue regions (CSF, GM, WM, and AD lesion) are consistent with a Neuroradiologist's examination. The quantitative analysis shows that patients with AD have more atrophy. The mean value of the volume of brain parenchyma of patients with AD is about 10% less than that of healthy controls.