Medical Image Registration (MIR) is a challenge that arises in many image processing applications when multiple images must be aligned. We deal with the case of multi-modalities that is approached as an optimization problem. In MIR deterministic algorithms are used mainly, the disadvantage is that many of them get trapped in local optima, especially in multi-modal registration. This work aims to overcome this disadvantage using Scatter Search and Particle Swarm as optimization algorithms, with the mutual information approach proposed by Mattes et. al. The proposed optimizers were tested and contrasted with Reference Algorithms. A multi-modal rigid 3D / 3D of brain medical image registration scheme was implemented, and it was validated in RIRE project. The qualitative and quantitative validation of the results was satisfactory; the results demonstrated the accuracy and applicability of the proposed methods in comparison to conventional methods, as well as not being trapped in local optima.
In this paper, we propose a unified approach for document segmentation. Differently of others techniques that segment images without a priori knowledge about the classes to be segmented, this approach carries out a previous learning of what must be segmented. The learning is carried out using only two images, the original one and its ideal segmented version. This stage generates a decision matrix, which is used to extract the similar semantic information in new images. The knowledge acquired in the decision matrix is explored by means of KNN strategy. Performed tests on different types of document images, like signature, postal envelopes and old document databases for instance, showed significant and promising results. It must be emphasized that this learning segmentation approach is completely automatic, does not require heuristics, and may transform the subjective human operator's knowledge into an automatic process and reproduce it.
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