Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2007.383032
Title: Model-guided segmentation of 3D neuroradiological image using statistical surface wavelet model
Authors: Li, Y.
Tan, T.-S. 
Volkau, H.
Nowinski, W.L.
Issue Date: 2007
Source: Li, Y.,Tan, T.-S.,Volkau, H.,Nowinski, W.L. (2007). Model-guided segmentation of 3D neuroradiological image using statistical surface wavelet model. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2007.383032
Abstract: 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 obtained through human expert. These show that our method is robust, computationally efficient and achieves a high degree of segmentation accuracy. © 2007 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/40812
ISBN: 1424411807
ISSN: 10636919
DOI: 10.1109/CVPR.2007.383032
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