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• W2003282940 abstract "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." @default.
• W2003282940 created "2016-06-24" @default.
• W2003282940 creator A5017510847 @default.
• W2003282940 creator A5028623087 @default.
• W2003282940 date "2005-02-01" @default.
• W2003282940 modified "2023-10-14" @default.
• W2003282940 title "An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours" @default.
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• W2003282940 doi "https://doi.org/10.1016/j.media.2004.05.001" @default.
• W2003282940 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15581809" @default.
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