| Medical image segmentation is a sufficiently complex problem that no single strategy has proven to be completely effective. Historically, region growing, clustering, and edge tracing have been used and while significant steps have been made in the first two, research into automatic, recursive, edge following has not kept pace. In this thesis, a new, advanced, edge tracing strategy based on recursive, target tracking algorithms and suitable for use in segmenting magnetic resonance (MR) and computed tomography (CT) medical images is presented.; This work represents the first application of recursive, target-tracking-based, edge tracing to the segmentation of MR and CT images of the head. Three algorithms representing three stages of development are described. In the third stage, pixel classification data are combined with edge information to guide the formation of the object boundary, and smooth, subpixel-resolution contours are obtained. Results from tests in images containing noise, intensity nonuniformity, and partial volume averaging indicate that the edge tracing algorithm can produce segmentation quality comparable to that from methods based on clustering and active contours, when closed contours can be formed. In addition, low-contrast boundaries can be identified in cases where the other methods may fail, indicating that the information extracted by the edge tracing algorithm is not a subset of that from the other approaches. Additional investigation may allow: (1) the use of knowledge to further guide the segmentation process; and, (2) the formation of multiple segmentation interpretations to be provided as output to the operator or as input to higher-level, automatic processing.; A literature review describing the most common medical image segmentation algorithms is also provided. Three generations of development are defined as a framework for classifying these algorithms. |
