Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

A Riemannian Framework for Shape Analysis of Subcortical Brain Structures

Xie, Shuisheng
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1375286022

Abstract Details

2013, Doctor of Philosophy (PhD), Ohio University, Electrical Engineering & Computer Science (Engineering and Technology).
Measuring the volumetric and morphological changes of brain structures in MR images can provide a reliable basis for diagnosing and studying Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Currently, the prevailing morphometric analysis frameworks are voxel-based morphometry (VBM), deformation-based morphometry (DBM) and tensor-based morphometry (TBM), which all involve a low-order spatial normalization procedure to project the computed voxel tissue map or motion field into a standard space. Shape and deformation information in local areas tend to be smoothed or even wiped out during the averaging process. In order to address the drawbacks of VBM/DBM/TBM, a novel local shape-based morphometry (SBM) framework on individual structures is developed in this dissertation, to measure the diff erence among a collection of anatomical images for individual subjects. SBM consists of three components: (1) a shape representation; (2) shape matching and comparison under Riemannian shape spaces; and (3) shape classifications and group analysis based on a well defined shape distance metric. Two di fferent sets of solutions have been developed for the three components. The first approach relies on an information rich shape representation based on skeleton (IRS), and the dissimilarity between two shapes is defined as the geodesic distance connecting their projections on a shape manifold. The second model is based on a Riemannian meridian shape (RMS) representation. Stemmed from spectral graph theory, RMS possesses the merit of capturing the salient structural properties along the direction of maximal shape variation. Group clustering, based on pair-wise shape distances, is carried out through advanced manifold learning techniques. Experiments with synthetic shapes and subcortical brain structures demonstrate the e ectiveness of our framework in calculating the distances among di erent shapes and serving as a potential anatomical biomarker for neurodegenerative diseases, including AD.
Jundong Liu (Advisor)
106 p.
Biomedical Engineering; Biomedical Research; Computer Science; Electrical Engineering
Riemannian; Shape Analysis; Shape Classification; Shape Space; Subcortical Structures; Brain; Alzheimer; Meridian

Recommended Citations

Citations

  • Xie, S. (2013). A Riemannian Framework for Shape Analysis of Subcortical Brain Structures [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1375286022

    APA Style (7th edition)

  • Xie, Shuisheng. A Riemannian Framework for Shape Analysis of Subcortical Brain Structures. 2013. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1375286022.

    MLA Style (8th edition)

  • Xie, Shuisheng. "A Riemannian Framework for Shape Analysis of Subcortical Brain Structures." Doctoral dissertation, Ohio University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1375286022

    Chicago Manual of Style (17th edition)

ohiou1375286022
1,111
© 2013, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.