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Titlebook: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009; 12th International C Guang-Zhong Yang,David Hawkes,Chris Taylor

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发表于 2025-3-21 17:12:01 | 显示全部楼层 |阅读模式
书目名称Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009
副标题12th International C
编辑Guang-Zhong Yang,David Hawkes,Chris Taylor
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009; 12th International C Guang-Zhong Yang,David Hawkes,Chris Taylor
描述The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009. Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The second volume includes 134 papers divided in topical sections on shape modelling and analysis; motion analyysis, physical based modelling and image reconstruction; neuro, cell and multiscale image analysis; image analysis and computer aided diagnosis; and image segmentation and analysis.
出版日期Conference proceedings 2009
关键词Navigation; classification; computer; image analysis; modeling; robot; robotics; tissue
版次1
doihttps://doi.org/10.1007/978-3-642-04271-3
isbn_softcover978-3-642-04270-6
isbn_ebook978-3-642-04271-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Statistical Location Model for Abdominal Organ Localizationg with the spine and their relative locations remain relatively stable, we built a statistical location model (SLM) and applied it to abdominal organ localization. The model is a point distribution model which learns the pattern of variability of organ locations relative to the spinal column from a
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Organ Segmentation with Level Sets Using Local Shape and Appearance Priorseity of structures outside of an organ of interest. However, most of these methods rely on landmark based segmentation, which has certain drawbacks. We propose to perform organ segmentation with a novel level set algorithm that incorporates local statistics via a highly efficient point tracking mech
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Airway Tree Extraction with Locally Optimal Pathsst to commonly used region growing based approaches that only search the space of the immediate neighbors. The result is a much more robust method for tree extraction that can overcome local occlusions. The cost function for obtaining the optimal paths takes into account of an airway probability map
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A Deformation Tracking Approach to 4D Coronary Artery Tree Reconstructiones acquired by a rotating C-arm. Our algorithm starts from a 3D coronary tree that was reconstructed from images of one cardiac phase. Driven by gradient vector flow (GVF) fields, the method then estimates deformation such that projections of deformed models align with X-ray images of corresponding
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Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data. Cone beam computed tomography (CBCT), often also called digital volume tomography (DVT), is increasingly utilized in maxillofacial or dental imaging. Compared to conventional CT, however, soft tissue discrimination is worse due to a reduced dose. Thus, small structures like the alveolar nerves are
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