Blood Vessel Segmentation from MRA Based on Boltzmann Theory

Segmentation is one of the most challenging problems in the field of medical image analysis, and blood vessels are especially difficult to extract. In this paper, we propose a novel method for segmentation of cerebral blood vessels from magnetic resonance angiography (MRA) images based on Boltzmann theory. The method is composed of three major steps: first, power-law transformation is applied to enhance blood vessels for their weak local contrast. Then a threshold value selected from a histogram analysis with a polyline splitting algorithm is employed to process the enhanced images in order to segment blood vessel regions. Then class region growing algorithm based on Boltzmann theory is adopted to extract blood vessels from background. Results on head MRA datasets demonstrate the availability of the method.