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In the existing various kinds of active contour model methods about image segmentation, level set method has been widely used because of its powerful capabilities of topological transformation. Because of the global computation, level set has accurate shape description effect. On the other hand, it makes the segmentation result sensitive to noise. In this paper, we propose a simple modified method...
Based on analysis of contours of irregular region, and according to the characteristic that massive continuous code and the same specific code combination are usually contained in a region boundary's vertex chain code, a new effective contour tracking algorithm and representation method based on the pixel vertex matrix is proposed. Moreover, we re-encoding the new vertex chain code using a Huffman...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
Brain tumor segmentation is an important image processing step in diagnosis, treatment planning, and follow-up studies of Glioblastoma (GBM). However it is still a challenging task due to varying in size, shape, location, and image intensities within and around the tumor. In this paper, we propose a new brain tumor segmentation method for T1-weighted MR brain images based on an improved level set...
Medical image registration methods based on the maximization of mutual information have shown promising results. But it needs much computing time. This paper presents a new method to rigid registration based on the combination of scale images and mutual information. Firstly, the concept of area morphology is introduced. And then, the scale images are constructed with the area morphological operators...
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