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Titlebook: Information Processing in Medical Imaging; 22nd International C Gábor Székely,Horst K. Hahn Conference proceedings 2011 Springer-Verlag Gmb

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书目名称Information Processing in Medical Imaging
副标题22nd International C
编辑Gábor Székely,Horst K. Hahn
视频video
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Information Processing in Medical Imaging; 22nd International C Gábor Székely,Horst K. Hahn Conference proceedings 2011 Springer-Verlag Gmb
描述This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.
出版日期Conference proceedings 2011
关键词computational anatomy; fMRI classification; radiation therapy; spatial-temporal registration; tree class
版次1
doihttps://doi.org/10.1007/978-3-642-22092-0
isbn_softcover978-3-642-22091-3
isbn_ebook978-3-642-22092-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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0302-9743 sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.978-3-642-22091-3978-3-642-22092-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Segmentation of 3D RF Echocardiography Using a Multiframe Spatio-temporal Predictorthm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from 6 canine studies.
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Optimal Graph Based Segmentation Using Flow Lines with Application to Airway Wall Segmentation and larger area of overlap than are obtained with recently published graph based methods..Airway abnormality measurements obtained with the method on 480 scan pairs from a lung cancer screening trial are reproducible and correlate significantly with lung function.
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Parameterization-Invariant Shape Statistics and Probabilistic Classification of Anatomical Surfacesuccess of this model through improved random sampling and a higher classification performance. We study brain structures and present classification results for Attention Deficit Hyperactivity Disorder. Using the mean and covariance structure of the data, we are able to attain an 88% classification rate.
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