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The brain ventricles are surrounded by periventricular structures that are affected by dementia which results in neurodegenerative disorder such as Alzheimer's Disease (AD). The change in morphology of these structures must effect the shape and volume of Corpus Callosum (CC). These alterations in morphology of CC are considered to be a significant image biomarker for the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) subjects. Shape descriptors provide useful information about change in morphology of various brain structures during disease progression. In this work, Lattice Boltzmann criterion based hybrid level set method (LSM) is used to segment CC. Geometric and pseudo-Zernike moment measures are extracted from the segmented area of CC and are statistically analyzed using Statistical Package for Social Science (SPSS). The performance metric of significant moments is validated using machine learning algorithms. Results demonstrate that, hybrid level set is able to delineate CC and the segmented images are in high correlation with ground truth images. High accuracy value of 85.0% has been achieved using Multilayer Perceptron (MLP) classifier for Healthy Control (HC) versus AD subjects. Thus, moments are able to classify MCI from HC and AD subjects with high accuracy and hence the results are found to be clinically significant.

Proposal of a Machine Learning Approach to Differentiate Mild and Alzheimer's Condition in MR Images Using Shape Changes in Corpus Callosum.