Segmentation and analysis of corpus callosum in Alzheimer mr images using total variation based diffusion filter and level set method

Alzheimer's Disease (AD) is a common form of dementia that affects gray and white matter structures of brain. Manifestation of AD leads to cognitive deficits such as memory impairment problems, ability to think and difficulties in performing day to day activities. Although the etiology of this disease is unclear, imaging biomarkers are highly useful in the early diagnosis of AD. Magnetic resonance imaging is an indispensible non-invasive imaging modality that reflects both the geometry and pathology of the brain. Corpus Callosum (CC) is the largest white matter structure as well as the main inter-hemispheric fiber connection that undergoes regional alterations due to AD. Therefore, segmentation and feature extraction are predominantly essential to characterize the CC atrophy. In this work, an attempt has been made to segment CC using edge based level set method. Prior to segmentation, the images are pre-processed using Total Variation (TV) based diffusion filtering to enhance the edge information. Shape based geometric features are extracted from the segmented CC images to analyze the CC atrophy. Results show that the edge based level set method is able to segment CC in both the normal and AD images. TV based diffusion filtering has performed uniform region specific smoothing thereby preserving the texture and small scale details of the image. Consequently, the edge map of CC in both the normal and AD are apparently sharp and distinct with continuous boundaries. This facilitates the final contour to correctly segment CC from the nearby structures. The extracted geometric features such as area, perimeter and minor axis are found to have the percentage difference of 5.97%, 22.22% and 9.52% respectively in the demarcation of AD subjects. As callosal atrophy is significant in the diagnosis of AD, this study seems to be clinically useful.