An approach to automatic segmentation of two-dimensional brain images from X-ray (CT) and magnetic resonance tomography (MR) is presented. Based on an image model of smooth regions separated by scaled discontinuities we derive computational processes for the problems of edge detection, contour grouping and smooth image reconstruction. We therefore employ advanced concepts of computer vision research such as scale-space analysis and deterministic and stochastic frameworks regularizing ill-posed inverse problems.
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