The formation of a plaque in one or both of the internal carotid arteries poses a serious threat to the lives of those in whom it occurs. This thesis describes a technique designed to detect level of occlusion and provide topological information about such plaques. In order to negate the cost of specialised hardware, only the sound produced by blood-flow around the occlusion is used; this raises problems that prevent the application of existing medical imaging techniques, however, these can be overcome by the application of a nonlinear technique that takes full advantage of the discrete nature of digital computers. Results indicate that both level of occlusion and presence or absence of various topological features can be determined in this way. Beginning with a review of existing work in medical-imaging and in more general but related techniques, the EPI process of Friden (2004) is identified as the strongest approach to a situation where it is desirable to work with both signal and noise yet avoid the computational cost and other pitfalls of established techniques. The remained of the thesis discusses attempts to automate the EPI process which, in the form given by Frieden (2004), requires a degree of human mathematical creative problem-solving. Initially, a numerical-methods inspired approach based on genetic algorithms was attempted but found to be both computationally costly and insufficiently true to the nature of the EPI equations. A second approach, based on the idea of creating a formal system allowing entropy, direction and logic to be manipulated together proved to lack certain key properties and require an amount of work beyond the scope of the project described in this thesis in order to be extended into a form that was usable for the EPI process. The approach upon which the imaging system described is ultimately built is based on an abstracted form of constraint-logic programming resulting in a cellular-automaton based model which is shown to produce distinct images for different sizes and topologies of plaque in a reliable and human-interpretable way.

Thesis (Ph. D.)--State University of New York at Buffalo, 2001. / "September, 2001." Includes bibliographical references (leaves 129-137). Also available in print.

Thesis (M. Sc.)--York University, 1999. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 90-93). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ39223.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 128-136).

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 95-100).

Thesis (Ph D.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 121-133).

Thesis (Ph. D.)--University of Hong Kong, 2005.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 52-55). Also available in electronic version.

For detecting difficult objects, such as hands, an algorithm is presented that uses tokens and a grammar. Tokens are found by employing a new scale space edge detector that finds scale invariant features at object boundaries. First, the scale space is constructed. Then edges at each scale are found and the scale space is flattened into a single edge image. To detect a hand pattern, a grammar is defined using curve tokens for finger tips and wedges, and line tokens for finger sides. Curve tokens are found by superimposing a curve model on the scale space edge image and scoring its fit. Line tokens are found by using a modified Burns line finder. A hand pattern is identified by parsing these tokens using a graph based algorithm. On a database of 200 images of finger tips and wedges, finger tip curves are detected 85% of the time, and wedge curves are detected 70% of the time. On a database of 287 images of open hands against cluttered backgrounds, hands are correctly identified 70% of the time.