Download Abdominal Imaging. Computational and Clinical Applications: by Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland, PDF

By Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland, Mingxing Hu, Darren J. Boone (auth.), Hiroyuki Yoshida, Georgios Sakas, Marius George Linguraru (eds.)

This publication constitutes the completely refereed post-conference lawsuits of the 3rd overseas Workshop on Computational and medical functions in belly Imaging, held along with MICCAI 2011, in Toronto, Canada, on September 18, 2011. The 33 revised complete papers awarded 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 stomach anatomy.

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Additional info 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

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A. Caan2, Manon L. Ziech2, Japp Stoker2, Lucas J. van Vliet1, and Frans M. nl Abstract. Inflammatory bowel diseases (IBD) constitute one of the largest healthcare problems in the Western World. Grading of the disease severity is important to determine treatment strategy and to quantify the response to treatment. The Time Injection Curves (TICs) after injecting a contrast agent contain important information on the degree of inflammation of the bowel wall. However, respiratory and peristaltic motions complicate an easy analysis of such curves since spatial correspondence over time is lost.

Subsequently, we selecteed those 3D images that corresponded to the local miniima of the oscillatory curve baseed on the Gaussian-weighted second-order derivative. T This delivers a gated subset of th he original data, representing volumes in the same phase of respiratory cycle. Howeverr, the images are not well registered, particularly duee to bowel motion. 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.

5 (4) Expectation Maximization and Subtraction After the partial probabilities were computed, a Gaussian mixture model expectation maximization algorithm was used to update the probabilities on the basis of intensity 22 N. Panjwani et al. and neighboring probabilities. The probability, time t+1 time step is: , , of voxel i being in class j at , , 2 2 , 2 , , , (5) where is the intensity for voxel i, , , and are the mean, variance and weight for class j at time step t, , is the probability of class j for voxel i at time t, and lastly , and , are the mean and variance of the probability for class j on the first order neighbors around voxel i.

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