A bayesian approach to in vivo kidney ultrasound contour detection using Markov Random Fields

Abstract

Automatic detection of structures in medical images is of great importance for the implementation of tools that can obtain accurate measurements for an eventual diagnosis. In this paper, a new method for the creation of such tools is presented. We focus on in vivo kidney ultrasound, a target in which classical methods fail due to the inherent difficulty of such an imaging modality and organ. The proposed method operates on every slice by detecting kidney contours under a probabilistic Bayesian framework. We make use of Markov Random Fields ideas to model the problem and find the solution. A computer easy-to-use interface to the model is also presented.