Shape-Driven 3d segmentation using spherical wavelets

Authors:
Delphine Nain;Steven Haker;Aaron Bobick;Allen Tannenbaum
Affiliations:
College of Computing, Georgia Institute of Technology, Atlanta;Department of Radiology, Brigham and Women’s Hospital, Boston;College of Computing, Georgia Institute of Technology, Atlanta;Electrical Engineering, Georgia Institute of Technology, Atlanta
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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Abstract

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.