By Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland, Mingxing Hu, Darren J. Boone (auth.), Hiroyuki Yoshida, Georgios Sakas, Marius George Linguraru (eds.)
ISBN-10: 3642285570
ISBN-13: 9783642285578
This e-book constitutes the completely refereed post-conference complaints of the 3rd overseas Workshop on Computational and scientific functions in belly Imaging, held along with MICCAI 2011, in Toronto, Canada, on September 18, 2011. The 33 revised complete papers offered have been rigorously reviewed and chosen from forty submissions. The papers are equipped in topical sections on digital colonoscopy and CAD, belly intervention, and computational belly anatomy.
Read Online or Download Abdominal Imaging. Computational and Clinical Applications: Third International Workshop, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, September 18, 2011, Revised Selected Papers PDF
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Additional resources for Abdominal Imaging. Computational and Clinical Applications: Third International Workshop, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, September 18, 2011, Revised Selected Papers
Sample text
However, respiratory and peristaltic motions complicate an easy analysis of such curves since spatial correspondence over time is lost. Therefore, the data analysis should comprise a 3D motion correction procedure. Previous work included motion tracking in rats [1] and 2D non-rigid registration of cardiac data [2]. We propose a 3D non-rigid motion correction method that robustly extracts time intensity curves from bowel segments in free-breathing abdominal DCE-MRI data. 0T Philips Intera scanner using a 3D spoiled gradient echo sequence.
2 Random Forests An RF classifier is an ensemble of decision trees. Let T denote the set of all trees, C the set of input classes (TP or FP), and L the leaves of a tree. For each tree t∈T , the input data are subsampled by use of out-of-bag bootstrapping. Suppose Dt denotes bootstrapped input data of t. At the root node, child nodes l and r are created that divide the input data into disjoint subsets Dt = Dl ∪ Dr (Dl ∩ Dr = ∅). This division is based on a node test, where the feature is chosen from a randomly sampled subset of all input features.
Fig. 2. Illustration of the oscilllatory of Dn. The black rounds correspond to the selected suubset of images that are in the same breathing phase. 3 Image Registration n A non-rigid registration pro ocedure was adopted to compensate for the remaining m misalignment. Thereby, we first selected a reference dynamic from the subset of images that 3D Non-rigid Motion Correction of Free-Breathing Abdominal DCE-MRI Data 47 are in the same respiratory phase. We chose as a reference the one dynamic with the lowest accumulated SSD with respect to the other dynamics within the subset: ∑ ∑ (2) (3) Here Ds is the accumulated SSD for dynamic s to the other volumes in the subset.
Abdominal Imaging. Computational and Clinical Applications: Third International Workshop, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, September 18, 2011, Revised Selected Papers by Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland, Mingxing Hu, Darren J. Boone (auth.), Hiroyuki Yoshida, Georgios Sakas, Marius George Linguraru (eds.)
by Thomas
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