In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.

Abstract. In the paper, a new evolutionary approach to induction of oblique decision trees is described. In each non-terminal node, the specialized evolutionary algorithm is applied to search for a splitting hyperplane. The feature selection is embedded into the algorithm, which allows to eliminate redundant and noisy features at each node. The experimental evaluation of the proposed approach is presented on both synthetic and real datasets.

This paper shows the influence of computed tomography slice thickness on textural parameters by simulating realistic images issued from: 1) a 3D model of vascular tree, with structural and functional features and in which angiogenesis is related to the organ growth; 2) a projection/reconstruction process using fast Fourier transform. Texture analysis is performed by means of second-order statistics and gradient based methods.