Brian G. Johnston
Three Dimensional Multispectral Stochastic Image Segmentation
| Degree: | M.Sc. |
| Type: | thesis |
| Year: | 1994 |
| Supervisors: | Kellogg S. Booth and Stella Atkins |
| Electronic: |
![[PDF]](/labs/imager/th/icons/pdf.gif){kind=icon} , 1347606 bytes |
| Hardcopy: | 137 pages |
Abstract
Current methods of lesion localization and quantification from magnetic resonance imaging, and other methods of computed tomography fall short of what is needed by clinicians to accurately diagnose pathology and predict clinical outcome. We investigate a method of lesion and tissue segmentation which uses stochastic relaxation techniques in three dimensions, using images from multiple image spectra, to assign partial tissue classification to individual voxels. The algorithm is an extension of the concept of Iterated Conditional Modes first used to restore noisy and corrupted images. Our algorithm requires a minimal learning phase and may incorporate prior organ models to aid in the segmentation. The algorithm is based on local neighbourhoods and can therefore be implemented in parallel to enhance its performance. Parallelism is achieved through the use of a dataflow image processing development package which allows multiple servers to execute in parallel.@MastersThesis{Johnston1994,
author = {Brian G. Johnston, M.Sc},
title = {Three Dimensional Multispectral Stochastic Image Segmentation},
school = {UBC},
year = {1994},
supervisor = {Kellogg S. Booth and Stella Atkins},
}