Abstract: The objective to develop some algorithms with new techniques of image processing for the automatic segmentation of the liver using magnetic resonance images. The methodology is based in a descriptive description was proposed that allows to combine the information of multiple channels using statistical models that have as a central point the multivariate and multisequence gaussian distribution. In this way, we will approach the spatial distribution having as a central point the intensity values in the different sequences and, therefore, we will be able to capture the variability of the data in each sequence at that moment. The results are based on the segmentation references and the proposed evaluation metrics to be able to validate the development different methods for segmentation were applied as inputs and what was obtained as a result of the segmentation originated a group of images that correspond to each of the cuts that have maximum resolution in the obtained sequences. All the images obtained here including the segmentation referred to, must be binary and their pixels must be marked with 1 (liver) or 0 (without liver). In conclusions the segmentation method that we propose here will consist of an active contour modeling in 2D and 3D and that will be developed in images that will be produced based on a new developed descriptor, having as an important point to minimize the image with an Approximation that is dual to the variational problem which should give us good results in the segmentation process.Abstract: The objective to develop some algorithms with new techniques of image processing for the automatic segmentation of the liver using magnetic resonance images. The methodology is based in a descriptive description was proposed that allows to combine the information of multiple channels using statistical models that have as a central point the multiva...Show More
