Development of a unified probabilistic framework for segmentation and recognition of semi-rigid objects in complex backgrounds via deformable shape models

This dissertation presents the development, implementation, and application of a unified probabilistic shape and appearance model (PSAM) algorithm for boundary-based segmentation and recognition of semirigid objects on complex backgrounds. The boundary position is iteratively adjusted to fit a new object based on a priori information gathered from a training set. PSAM is derived from compound Bayesian decision theory, and the formulation is general enough that it can be used as a starting point to derive a variety of other probabilistic boundary-finding techniques. The motivation for developing PSAM arose from a need to segment and recognize semirigid anatomic structures within medical images that have faint and/or missing edge information.; PSAM contains three specific model components: (1) a global shape model (GSM), (2) a local shape model (LSM), and (3) a gray-level model (GLM). All three of the PSAM components are optimized simultaneously when boundary searches are performed within new images. PSAM is formulated so that the influence of each of these components on the final boundary position can be controlled by the system operator. This allows the same PSAM algorithm to be used in applications with predictable global shape and relatively poor object edge strength, as well as in other applications where global shape is unpredictable but object edges are prominent.; The performance of the PSAM algorithm is summarized on both synthetic and real-world data. The results of three cases of real medical image data segmentations are presented. These cases include X-ray tomographic images of anatomic structures within laboratory mice. Specifically, the skull, the heart and lungs, and the kidneys are segmented using PSAM and ASM; and the results of the two algorithms are directly compared. In all cases the PSAM algorithm performed well and in fact, outperformed ASM by a substantial margin. It is shown that PSAM has a much larger degree of success than ASM on the most difficult segmentation cases. The PSAM performance is summarized, and a variety of future research topics are suggested that could lead to improved performance and broader applicability.