Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.
Alzheimer's disease (AD) is associated with the degeneration of cerebral cortex, which results in focal volume change or thinning in the cerebral cortex in magnetic resonance imaging (MRI). Therefore, the measurement of the cortical thickness is important for detection of the atrophy related to AD. Our purpose was to develop a computerized method for automated measurement of the cortical thickness for three-dimensional (3-D) MRI. The cortical thickness was measured with normal vectors from white matter surface to cortical gray matter surface on a voxel-by-voxel basis. First, a head region was segmented by use of an automatic thresholding technique, and then the head region was separated into the cranium region and brain region by means of a multiple gray level thresholding with monitoring the ratio of the first maximum volume to the second one. Next, a fine white matter region was determined based on a level set method as a seed region of the rough white matter region extracted from the brain region. Finally, the cortical thickness was measured by extending normal vectors from the white matter surface to gray matter surface (brain surface) on a voxel-by-voxel basis. We applied the computerized method to high-resolution 3-D T1-weighted images of the whole brains from 7 clinically diagnosed AD patients and 8 healthy subjects. The average cortical thicknesses in the upper slices for AD patients were thinner than those for non-AD subjects, whereas the average cortical thicknesses in the lower slices for most AD patients were slightly thinner. Our preliminary results suggest that the MRI-based computerized measurement of gray matter atrophy is promising for detecting AD.
Diffusion tensor (DT) MRI provides the directional information of water molecular diffusion, which can be utilized to estimate the connectivity of white matter tract pathways in the human brain. Several white matter tractography methods have been developed to reconstruct the white matter fiber tracts using DT-MRI. With conventional methods (e.g., streamline techniques), however, it would be very difficult to trace the white matter tracts passing through the fiber crossing and branching regions due to the ambiguous directional information with the partial volume effect. The purpose of this study was to develop a new white matter tractography method which permits fiber tract branching and passing through crossing regions. Our tractography method is based on a three-dimensional (3D) directional diffusion function (DDF), which was defined by three eigenvalues and their corresponding eigenvectors of DT in each voxel. The DDF-based tractography (DDFT) consists of the segmentation of white matter tract region and fiber tracking process. The white matter tract regions were segmented by thresholding the 3D directional diffusion field, which was generated by the DDF. In fiber tracking, the DDFT method estimated the local tract direction based on overlap of the DDFs instead of the principal eigenvector, which has been used in conventional methods, and reconstructed tract branching by means of a one-to-many relation model. To investigate the feasibility and usefulness of the DDFT method, we applied it to DT-MRI data of five normal subjects and seven patients with a brain tumor. With the DDFT method, the detailed anatomy of white matter tracts was depicted more appropriately than the conventional methods.