Appearance models for robust segmentation of pulmonary nodules in 3d LDCT chest images

Authors:
Aly A. Farag;Ayman El-Baz;Georgy Gimel’farb;Robert Falk;Mohamed A. El-Ghar;Tarek Eldiasty;Salwa Elshazly
Affiliations:
Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, Auckland, New Zealand;Director, Medical Imaging Division, Jewish Hospital;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY
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

To more accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of visual appearance of small 2D and large 3D pulmonary nodules are used to control evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with pairwise interaction analytically identified from a set of training nodules. Appearance of the nodules and their background in a current multi-modal chest image is also represented with a marginal probability distribution of voxel intensities. The nodule appearance model is isolated from the mixed distribution using its close approximation with a linear combination of discrete Gaussians. Experiments with real LDCT chest images confirm high accuracy of the proposed approach.