Advances in computational vision and medical image - download pdf or read online

By Tavares J., Jorge R. (eds.)

ISBN-10: 1402090854

ISBN-13: 9781402090851

The current booklet includes prolonged types of papers provided within the overseas convention VIPIMAGE 2007 – ECCOMAS Thematic convention on Computational imaginative and prescient and scientific photograph, held in Faculdade de Engenharia da Universidade do Porto, in 17-19 of October 2007. This convention was once the 1st ECCOMAS thematic convention on computational imaginative and prescient and clinical photograph processing. It covers issues similar with picture processing and research, scientific imaging and computational modeling and simulation, contemplating their multidisciplinary. This e-book collects the state of the art study, equipment and new rules almost about computational imaginative and prescient and scientific photo processing contributing for the advance of those parts of data.

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2. Using curation and geometry segmentation, we identified the portions which most prominently correspond to the portions of a template heart model while pruning away the undesired portions. 14C shows the relevant portions that have high correlation with the corresponding portions of the template geometry. Parallel to the processing of the patient heart, we also performed geometric analysis of the template heart model. We construct the one dimensional skeletal structure of all the six components of the template, as well as we segmented the template into aorta, pulmonary artery, right and left ventricle and atrium (Fig.

The most commonly used methods utilize global contrast manipulation based on global [31, 32] or local histogram equalization [31–34], retinex model [35, 36] and wavelet decomposition [37, 38]. We have developed a fast method for image contrast enhancement [39] based on a localized version of the classical contrast manipulation [31, 32]. In this method, we design an adaptive transfer function for each individual voxel, based on the intensities in a suitable local neighborhood. First, we compute the local statistics (local average, local minimum, and local maximum) for each voxel using a fast propagation scheme [40, 41].

In this method a contour is initialized from a pre-chosen seed point, and the contour is allowed to grow until a certain stopping condition is reached. Traditionally this method is designed for a single object segmentation. We present an approach based on an idea of “re-initialization” by simply regarding and classifying the critical points as seeds. Every seed initiates one contour and all contours start to grow simultaneously and independently. We further classify the critical points into clusters and merge the growing contours which are initiated by the critical points in the same cluster.

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Advances in computational vision and medical image processing by Tavares J., Jorge R. (eds.)

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