Active shape models—their training and application
Computer Vision and Image Understanding
A MRF Based Random Graph Modelling the Human Cortical Topography
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
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In this paper we present an original approach for the segmentation of MRI brain images which is based on a cooperation between low-level and high-level approches. MRI brain images are very difficult to segment mainly due to the presence of inhomogeneities within tissues and also due to the high anatomical variability of the brain topology between individuals. In order to tackle these difficulties, we have developped a method whose characteristics are : (i) the use of a priori knowledge essentially anatomical and model-based ; (ii) a multi-agent system (MAS) for low-level region segmentation ; (iii) a cooperation between a priori knowledge and low-level segmentation to guide and constrain the segmentation processes. These characteristics allow to produce an automatic detection of the main tissues of the brain. The method is validated with phantoms and real images through comparisons with another widely used approach (SPM).