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Titlebook: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006; 9th International Co Rasmus Larsen,Mads Nielsen,Jon Sporring Conf

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书目名称Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006
副标题9th International Co
编辑Rasmus Larsen,Mads Nielsen,Jon Sporring
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 2006; 9th International Co Rasmus Larsen,Mads Nielsen,Jon Sporring Conf
描述The 9th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2006, was held in Copenhagen, Denmark at the Tivoli Concert Hall with satellite workshops and tutorials at the IT University of Copenhagen, October 1-6, 2006. The conference has become the premier international conference with - depth full length papers in the multidisciplinary ?elds of medical image c- puting, computer-assisted intervention, and medical robotics. The conference brings together clinicians, computer scientists, engineers, physicists, and other researchers and o?ers a forum for the exchange of ideas in a multidisciplinary setting. MICCAI papers are of high standard and have a long lifetime. In this v- ume as well as in the latest journal issues of Medical Image Analysis and IEEE Transactions on Medical Imaging papers cite previous MICCAIs including the ?rst MICCAI conference in Cambridge, Massachusetts, 1998. It is obvious that the community requires the MICCAI papers as archive material. Therefore the proceedingsofMICCAIarefrom2005andhenceforthbeing indexedbyMedline. Acarefulreviewandselectionprocesswasexecutedinordertosecurethebest possible program for the MICCAI
出版日期Conference proceedings 2006
关键词Performance; Simulation; algorithms; biomedical data analysis; biomedical modeling; cardiac image analysi
版次1
doihttps://doi.org/10.1007/11866763
isbn_softcover978-3-540-44727-6
isbn_ebook978-3-540-44728-3Series 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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Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snakee complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. The first step was the extraction of texture features. The resulting feature space was used for classification, constructing a likelihood map
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Prostate Segmentation in 2D Ultrasound Images Using Image Warping and Ellipse Fittingation algorithm first uses image warping to make the prostate shape elliptical. Measurement points along the prostate boundary, obtained from an edge-detector, are then used to find the best elliptical fit to the warped prostate. The final segmentation result is obtained by applying a reverse warpin
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Fast Non Local Means Denoising for 3D MR Imagesising is a crucial step to increase image conspicuity and to improve the performances of all the processings needed for quantitative imaging analysis. The method proposed in this paper is based on an optimized version of the Non Local (NL) Means algorithm. This approach uses the natural redundancy o
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Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical D employed shape model is built from 32 samples using an optimization approach based on the minimum description length (MDL). Three different gray-value appearance models (plain intensity, gradient and normalized gradient profiles) are created to guide the search. The employed segmentation techniques
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Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adultsre is typically generated via manual tracing within this model. When model-based segmentation is applied to human cortical data, problems arise if left-right comparisons are desired. The asymmetry of the human cortex requires that both left and right models of a structure be composed in order to eff
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Image Diffusion Using Saliency Bilateral Filterprocessing algorithms such as image compression, segmentation and recognition. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries in the input images. The boundary saliency is estimated through a saliency meas
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