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Titlebook: Information Processing in Medical Imaging; 17th International C Michael F. Insana,Richard M. Leahy Conference proceedings 2001 Springer-Ver

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書目名稱Information Processing in Medical Imaging
副標(biāo)題17th International C
編輯Michael F. Insana,Richard M. Leahy
視頻videohttp://file.papertrans.cn/466/465152/465152.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Information Processing in Medical Imaging; 17th International C Michael F. Insana,Richard M. Leahy Conference proceedings 2001 Springer-Ver
描述This book constitutes the refereed proceedings of the 17th International Conference on Information Processing in Medical Imaging, IPMI 2001, held in Davis, CA, USA, in June 2001..The 54 revised papers presented were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on objective assessment of image quality, shape modeling, molecular and diffusion tensor imaging, registration and structural analysis, functional image analysis, fMRI/EEG/MEG, deformable registration, shape analysis, and analysis of brain structure.
出版日期Conference proceedings 2001
關(guān)鍵詞3D; Brain Mapping; Cardiac Images; Imaging (MRI); Magnetic Resonance; Mammography; Medical Image Computing
版次1
doihttps://doi.org/10.1007/3-540-45729-1
isbn_softcover978-3-540-42245-7
isbn_ebook978-3-540-45729-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2001
The information of publication is updating

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On the Difficulty of Detecting Tumors in Mammogramsrly good qualitative agreement. However we found that human performance was better than what would be expected if mammographic structure was assumed to be pure noise. This disagreement can be accounted for by using a simple scaling correction factor.
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Steps Toward a Stereo-Camera-Guided Biomechanical Model for Brain Shift Compensationakes into account neuro-anatomical constraints and is able to correspondingly deform all preoperatively acquired data. The system was tested on two sets of intraoperative MR scans, and an initial validation indicated that our approach reduced the error caused by brain shift.
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A Regularization Scheme for Diffusion Tensor Magnetic Resonance Imagesrection maps, while the latter makes use of the strong structural information embedded in the diffusion tensor image to drive a non-linear anisotropic diffusion process. The whole process is illustrated on synthetic and real data, and possible improvements are discussed.
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Study of Connectivity in the Brain Using the Full Diffusion Tensor from MRIpared within or between subjects. Unlike traditional tractography, we use all the data in the diffusion tensor at each voxel which is likely to increase robustness and make intersubject comparisons easier.
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Tracking Brain Deformations in Time-Sequences of 3D US ImagesD ultrasound (US) images. The automatic processing of this kind of images is of great interest for the development of innovative and low-cost image guided surgery tools. The difficulty relies both in the complex nature of the ultrasound image, and in the amount of data to be treated as fast as possible.
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