Model-Guided Segmentation of 3D Neuroradiological Image Using Statistical Surface Wavelet Model

This paper proposes a novel model-guided segmentation framework utilizing a statistical surface wavelet model as a shape prior. In the model building process, a set of training shapes are decomposed through the subdivision surface wavelet scheme. By interpreting the resultant wavelet coefficients as random variables, we compute prior probability distributions of the wavelet coefficients to model the shape variations of the training set at different scales and spatial locations. With this statistical shape model, 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 multiscale and spatial-localized manner. We have applied our method to segment cerebral caudate nuclei from MRI images. The experimental results have been validated with segmentations obta...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Yang Li, Tiow Seng Tan, Ihar Volkau, Wieslaw L. Nowinski
• Keywords: Computer Vision, CVPR 2007, Shape Variations, Statistical Shape Model, Subdivision Surface Wavelet, Surface Wavelet Model, Wavelet Shape Representation