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Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011; 14th International C Gabor Fichtinger,Anne Martel,Terry Peters Co

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书目名称Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011
副标题14th International C
编辑Gabor Fichtinger,Anne Martel,Terry Peters
视频video
概述State-of-the-art research.Fast-track conference proceedings.Unique visibility
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011; 14th International C Gabor Fichtinger,Anne Martel,Terry Peters Co
描述The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 819 submissions for presentation in three volumes. The third volume includes 82 papers organized in topical sections on computer-aided diagnosis and machine learning, and segmentation.
出版日期Conference proceedings 2011
关键词bioinformatics; computer aided diagnosis; image guided surgery; medical image analysis; visual simulatio
版次1
doihttps://doi.org/10.1007/978-3-642-23626-6
isbn_softcover978-3-642-23625-9
isbn_ebook978-3-642-23626-6Series 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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Aggregated Distance Metric Learning (ADM) for Image Classification in Presence of Limited Training Draining sets are then combined for image classification. We present a theoretical proof of the superiority of classification by ADM over BDM. Using both clinical (X-ray) and non-clinical (toy car) images in our experiments (with altogether 10 sets of different parameters) and image classification ac
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Robust Large Scale Prone-Supine Polyp Matching Using Local Features: A Metric Learning Approachcollapsed segmentation cases. Our automatic approach is extensively evaluated using a large multi-site dataset of 195 patient cases in training and 223 cases for testing. No external examination on the correctness of colon segmentation topology [2] is needed. The results show that we achieve signifi
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Targeted Optical Biopsies for Surveillance Endoscopiesndoscopy into several scenes and establishing cluster correspondences accross these videos. During the second run surveillance (.), the scene recognition is performed in . and . based on the cluster correspondences. Detailed experimental results demonstrate the feasibility of the proposed approach w
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Focal Biologically Inspired Feature for Glaucoma Type Classificationd 84.3% images from angle closure glaucoma. The accuracy could be improved close to 90% with more images included in the training. The results show that the focal biologically inspired feature is effective for automatic glaucoma type classification. It can be used to reduce workload of ophthalmologi
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Assessment of Regional Myocardial Function via Statistical Features in MR Imagesthe most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed met
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Simultaneous Segmentation and Grading of Hippocampus for Patient Classification with Alzheimer’s Disuccess rate of 89%. Finally, a comparison of several biomarkers was investigated using a linear discriminant analysis. Conclusion: Using the volume and the grade of the HC at the same time resulted in an efficient patient classification with a success rate of 90%.
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Mariam Afshin,Ismail Ben Ayed,Kumaradevan Punithakumar,Max W. K. Law,Ali Islam,Aashish Goela,Ian Rosent survey of the relevant technologies and environment, and will be of benefit to AI researchers engaged with interface design, and practitioners in the area of cultural heritage support and marketing..978-3-642-08824-7978-3-540-68755-9Series ISSN 1611-2482 Series E-ISSN 2197-6635
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