Medical image analysis using statistical shape model based on subdivision surface wavelet

Abstract

In this thesis, we propose a novel statistical shape model based on the shape representation using subdivision surface wavelets. It has three highly desirable properties of a statistical shape model: compact shape representation, multi-scale shape description and spatial-localization of the shape variation. We also develop a new model-guided segmentation framework utilizing this Statistical Surface Wavelet Model (SSWM) as a shape prior, in which the segmentation task is formulated as an optimization problem to best fit the statistical shape model with an input image. Due to the localization property of the wavelet shape representation both in scale and space, this multi-dimensional optimization problem can be efficiently solved in a multi-scale and spatially localized manner. We have applied our method to segment caudate nucleus and putamen from MRI. The results show that our segmentation method is robust, computationally efficient and achieves a high degree of segmentation accuracy.