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Titlebook: Computer Vision Approaches to Medical Image Analysis; Second International Reinhard R. Beichel,Milan Sonka Conference proceedings 2006 Spri

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書目名稱Computer Vision Approaches to Medical Image Analysis
副標(biāo)題Second International
編輯Reinhard R. Beichel,Milan Sonka
視頻videohttp://file.papertrans.cn/235/234019/234019.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision Approaches to Medical Image Analysis; Second International Reinhard R. Beichel,Milan Sonka Conference proceedings 2006 Spri
描述Medical imaging and medical image analysis are developing rapidly. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. This was the second time that a satellite workshop,solely devoted to medical image analysis issues, was held in conjunction with the European Conference on Computer Vision (
出版日期Conference proceedings 2006
關(guān)鍵詞3D imaging; Bayesian networks; Computer Vision; Ensembl; Image segmentation; Scale-invariant feature tran
版次1
doihttps://doi.org/10.1007/11889762
isbn_softcover978-3-540-46257-6
isbn_ebook978-3-540-46258-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2006
The information of publication is updating

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Comparative Analysis of Kernel Methods for Statistical Shape Learningthe statistics on a set of training shapes, which are then used for a given image segmentation task to provide the shape prior. In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized l
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