An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours.
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.
Martín-Fernández
Marcos
M
Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación of the University of Valladolid, 47011 Valladolid, Spain.
Alberola-López
Carlos
C
D001499:Bayes Theorem; D006801:Humans; D007668:Kidney / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D008962:Models, Theoretical; D011897:Random Allocation; D012680:Sensitivity and Specificity