Download e-book for iPad: Medical Image Computing and Computer-Assisted Intervention – by Yanwu Xu, Dong Xu, Stephen Lin, Jiang Liu, Jun Cheng, Carol
By Yanwu Xu, Dong Xu, Stephen Lin, Jiang Liu, Jun Cheng, Carol Y. Cheung, Tin Aung (auth.), Gabor Fichtinger, Anne Martel, Terry Peters (eds.)
The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed court cases of the 14th overseas convention on scientific photo Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. in accordance with rigorous peer reports, this system committee rigorously chosen 251 revised papers from 819 submissions for presentation in 3 volumes. The 3rd quantity contains eighty two papers geared up in topical sections on computer-aided prognosis and computing device studying, and segmentation.
Read Online or Download Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III PDF
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Extra info for Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III
867 that can be Learning from Only Positive and Unlabeled Data to Detect Lesions 15 Fig. 3. Lesion detection results. Color-coded labels on a stretched curved planar reformatted view of diﬀerent coronary arteries. explained by the fact that our novel method does not misclassify bifurcations, contrary to the one in . 142 conﬁrms that our overall performance is better. Another related work  lacks a clear deﬁnition of the evaluation measurement unit, making direct comparison uneasy. e. a lesion is correctly detected (TP) if at least one cross-section within the extent of the lesion is detected as diseased.
Where ωj is the corresponding weight of the j th feature group, l is the number of training samples and λ is used to control the sparsity of ω. In Eq. (1), the first term represents the regression error and the second term is a L1,2 -norm based regularizer to enforce group sparsity. Considering the features are intrinsically organized in groups, we use an L1,2 -norm based regularizer to select features from only a sparse set of groups. In the experiments, we use the group-lasso method in  to solve Eq.
Daoqiang Zhang, Guorong Wu, Hongjun Jia, and Dinggang Shen Probabilistic Multi-shape Segmentation of Knee Extensor and Flexor Muscles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shawn Andrews, Ghassan Hamarneh, Azadeh Yazdanpanah, Bahareh HajGhanbari, and W. Darlene Reid 570 579 587 595 603 611 619 627 635 643 651 Table of Contents – Part III Robust Skull Stripping of Clinical Glioblastoma Multiforme Data . . . William Speier, Juan E. Iglesias, Leila El-Kara, Zhuowen Tu, and Corey Arnold Multi-stage Learning for Robust Lung Segmentation in Challenging CT Volumes .