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 booklet constitutes the completely refereed post-conference court cases of the 3rd overseas Workshop on Computational and scientific functions in stomach Imaging, held along with MICCAI 2011, in Toronto, Canada, on September 18, 2011. The 33 revised complete papers provided have been rigorously reviewed and chosen from forty submissions. The papers are prepared 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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Computed Tomographic Colonography (Virtual Colonoscopy): A Multicenter Comparison with Standard Colonoscopy for Detection of Colorectal Neoplasia. Jama 291(14), 1713–1719 (2004) 2. : Computed Tomographic Virtual Colonoscopy Computer-Aided Polyp Detection in a Screening Population. Gastroenterology 129(6), 1832–1844 (2005) 3. : Colorectal Cancer Screening with CT Colonography, Colonoscopy, and Double-Contrast Barium Enema Examination: Prospective Assessment of Patient Perceptions and Preferences.

For an unseen sample, the RF classifier is tested by propagation of the sample through each tree. The predictions by the trees are aggregated into an RF prediction by majority vote. J. N¨ appi, D. Regge, and H. Yoshida Fig. 2. 3 The CAD System The CAD system is fully automated. A thick region encompassing the colonic mucosa is extracted automatically from input CTC data [5]. Volumetric shape features are calculated within the extracted region, and lesion candidates are detected by hysteresis thresholding of the shape features [6].

Our results were compared with images generated by the FICE images, which is the current enhancement technique supported by VCE reader software (RapidReader Ver. 6, [5]). First, the ROIs including abnormalities were extracted from the original images with a fixed window size of 128 × 128 pixels. For the evaluation, 60 images were prepared containing different types of abnormalities such as angiodysplasia, erosion, ulcers, and polyps. Four physicians, with rich experience in CE image analysis, were asked to assess the enhanced images according to two criteria: - Acceptable rate υ: determines whether the enhanced images are useful in clinical practice.

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