Multivariate Volumetric Morphometry for Characterizing Anatomical Discrepancy in MR Images of Different Groups

• Main Authors: Cheng-Chia Yang, 楊承嘉
• Other Authors: Yong-Sheng Chen
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/38286824912606794068

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 94 === Recently, voxel-based morphometry (VBM) has been widely applied to statistically infer the structural anomalies between the brains of two subject groups, in a voxel-by-voxel manner. This method is effective for mapping massive and centralized discrepancy. However, it may suffer from the poor sensitivity to subtle and widely-distributed discrepancy in brain structures. In this work, we propose a novel multivariate morphometry (MVM) method that can be used to delineate the anatomical discrepancy between two groups of MR images. Rather than voxel-by-voxel manner in VBM, the proposed MVM simultaneously considers all of the voxels in MR volumes and map the group differences by using the linear discriminant analysis to determine the most discriminant projection vector. Each element in the projection vector represents the discrimination weight of the corresponding voxel involved in the combination of the most discriminant components. This weight can thus be regarded as the significance level of the corresponding voxel when differentiating two groups of MR volumes. This multivariate approach is appropriate to characterize group discrepancy, particularly when the brain atrophy distributes widely. Moreover, we prove that the discriminability remains the same no matter the projection vector is calculated from the original MR volumes or from the smoothed ones. Hence we can simply use the original data without the interference of the blurring artifact caused by the smoothing operation. On the contrary, VBM method applies the Gaussian smoothing filter to reduce image noise as well as to incorporate spatial support from neighboring voxels. It is difficult to determine an appropriate kernel size for the smoothing filter because larger kernel can reduce more noise, but with the penalty of more smeared image. According to our experiments, we demonstrate the effectiveness of the proposed method by using the simulation data set containing artificial atrophy around the cerebellum area. Compared to the VBM method, the proposed MVM method can achieve a better sensitivity to subtle and widely-distributed variation of brain structure. When applied to a clinical study of SCA3 disease, the MVM method clearly reveals more significant atrophy in the disease-related areas within the brain volumes of the patient group, than the VBM method does.