| Shape modeling is an important area in computer vision and medical imaging. Shape models have proved valuable in the tasks of object representation, recognition, and classification. This thesis focuses on shape models of 3D surfaces extracted from volumetric images and their applications in medical image analysis.; This thesis presents a new framework for classification of 3D neuroanatomic structures using shape features, with the goal of deriving a shape-based medical classifier that has clinical value. This framework combines a powerful 3D surface modeling method, the spherical harmonics (SPHARM) description, with a set of effective pattern classification, feature selection, evaluation, and visualization techniques. This approach is shown to be effective using simulated shape data. It is also applied to real hippocampal data, and achieves good cross-validation accuracy (>90%) in detecting hippocampal shape changes in schizophrenia, which is competitive with the best results in previous studies. To help medical diagnosis in practice, a threshold-free receiver operating characteristic approach is employed as a means of evaluating the potential of a classifier. In addition, a new visualization approach is proposed for localizing discriminative patterns.; This thesis also considers two extensions of the above work. In one extension, the left and right hippocampi are treated as a single shape configuration, and a new multi-surface alignment algorithm is developed for aligning configurations of multiple surfaces simultaneously. This alignment algorithm is incorporated into our classification for hippocampal data. The best cross-validation accuracy achieved is 92%, supporting the idea that the shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. In the other extension, we focus on spherical parameterization, which is the key step for deriving a SPHARM description for a 3D object. Earlier techniques can be applied only to voxel surfaces. We propose CALD, a new spherical parameterization algorithm which makes our framework applicable to general triangle meshes. This new algorithm can be used for shape representation and manipulation purposes in different areas, including medical image analysis, computer vision, graphics, and multimedia databases. |
