期刊
MEDICAL IMAGE ANALYSIS
卷 9, 期 1, 页码 1-23出版社
ELSEVIER
DOI: 10.1016/j.media.2004.05.001
关键词
active contours; Markov random fields; Bayesian segmentation; US; deformation model
In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out. (C) 2004 Elsevier B.V. All rights reserved.
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