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For 3D medical images with strong similarities of interfacial gradients, a method is presented to bring local regional characteristics of segmented sections into adjacent sections waiting for segmentation and to guide the contour curves of the later to converge to actual boundary. The proposed method improves the stopping criterion of curve evolution through introduction of adjacent layer's prior...
According to the low calculating speed of Chan-Vese model for image segmentation caused by the iteration in process of evolution in the whole image region, a fast medical image segmentation method based on improved incremental variational level set is presented in this paper, in which incremental mode is adopted to get average gray value in iteration and a progressive iterative formula is used as...
Optimization of a similarity metric is an essential component in intensity-based medical image registration. In this paper, an improved variable neighborhood selection based particle swarm optimization (VNS-PSO) is proposed. The PSO algorithm is co-operative, population-based global search swarm intelligence mataheuristics. The improved version of PSO algorithm possesses better ability to escape from...
Registration for sequential images has significant meanings in clinical diagnosis and analysis. Many researches are concentrated on using higher-order mutual information (HMI) to fulfill the registration task. But HMI has some disadvantages, which limit the applications of HMI to a large degree. The paper proposes a new registration measure, called nonlinear correlation information entropy (NCIE),...
Unsupervised segmentation of volumetric data is still a challenging task. Recently, the level set methods have received a great deal of attention, which combine global smoothness with the flexibility of topology changes and offer significant advantages over conventional statistical classification. However, the level set methods suffer from heavy computational burden for a lot of iterations. We present...
Many investigators are incorporating medical image feature analysis into computer-aided diagnosis (CAD) systems to increase the precision and accuracy of characterization by radiologists. Searching medical databases for images similar to a given query image that corresponds to a case under current study and enabling access to those other clinical data and known diagnoses from those similar cases is...
A global optimization technique for image registration using the concept of nonlinear correlation information entropy (NCIE) as the matching criterion is presented. The method makes it possible to efficiently overcome the local minima problem utilizing the extremum property of NCIE. Furthermore, the improved downhill simplex algorithm incorporated variant accuracy tolerance can reduce the evaluation...
In this paper a new approach to the problem of rigid body registration is proposed, using a concept of nonlinear correlation information entropy (NCIE) as the new matching criterion. The presented method applies NCIE to measure the correlation degree between the image intensities of corresponding voxel in both images. Registration is achieved by adjustment of the relative position until the NCIE between...
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