Abstract: In this paper we propose a new Computer Vision technique to reconstruct the vascular wall in space using a deformable model-based technique and compounding methods, based in biplane angiography and intravascular ultrasound data jicsion. It is also proposed a generalpurpose three-dimensional guided interpolation method. The three dimensional centerline of the vessel is reconstructed from geometrically corrected biplane angiographies using automatic segmentation methods and snakes. The IVUS image planes are located in the threedimensional space and correctly oriented. A led interpolation method based in B-SurJaces and snakes isused to fill the gaps among image planes

Abstract: Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signicant improvement.

Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.