Neuroanatomical segmentation in MRI exploiting a priori knowledge

Date and time: 
Thursday, March 8, 2007 - 14:30
Location: 
220 Deschutes
Author(s):
Kai Li
University of Oregon
Host/Committee: 
  • Allen Malony, Computer Science (Chair)
  • Dejing Dou, Computer Science
  • Kent Stevens, Computer Science
  • Don M. Tucker, Psychology
Abstract: 

Neuroanatomical segmentation is a problem of extraction of a description of particular neuroanatomical structures of interest that reflects the morphometry (shape measurements) of the subject's neuroanatomy from any image rendering the neuroanatomical structures of the subject. This dissertation presents a set of algorithms for automatic extraction of cerebral white mater (WM) and gray matter (GM) as well as reconstruction of cortical surfaces from T1-weighted MR images.

Neuroanatomical segmentation presented in this dissertation is performed by an image analysis pipeline that steps through five major procedures: 1) the original MR image is processed by a new relative thresholding procedure and a new terrain analysis procedure such that all voxels are classified into one of the three types: WM, GM, and background; 2) the topology defects of the WM are eliminated by a new multiscale morphological topology correction algorithm; 3) cerebral WM is extracted from its superset with a new procedure called cell-complex-based morphometric analysis; 4) cerebral GM is extracted based on the prior cerebral WM extraction with a set of morphological image analysis procedures; 5) cortical surfaces are finally reconstructed preserving correct topology with an existing marching cube isosurface algorithm.

In this dissertation, we evaluated our neuroanatomical segmentation tool both quantitatively and qualitatively on a set of MR images with groundtruth or manual segmentation, compared the results of our tool with those of the other four tools, and demonstrated that the performance of our tool is highly accurate, robust, automatic and computationally efficient.

The advantages of our tool are mainly attributed to our extensive exploration of various structural, geometrical, morphological, and radiological a priori knowledge, either new or in new perspectives, which persists despite of image artifacts and inter-subject anatomical variations. By exploiting a priroi knowledge, we also demonstrated that it is a promising research direction of performing voxel classification prior to brain extraction, contrary to the traditional procedure of brain extraction followed by voxel classification. Finally, it's worth noting that the algorithms of voxel classification and morphological image analysis presented in this dissertation for neuroanatomical segmentation can be potentially applied in wider areas in computer vision.