Download e-book for kindle: Abdominal Imaging. Computational and Clinical Applications: by Holger R. Roth, Thomas E. Hampshire, Jamie R. McClelland,

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 ebook constitutes the completely refereed post-conference lawsuits of the 3rd foreign Workshop on Computational and scientific functions in belly Imaging, held along side MICCAI 2011, in Toronto, Canada, on September 18, 2011. The 33 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. The papers are equipped in topical sections on digital colonoscopy and CAD, stomach 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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3 compares the enhancement results of the original ROI of size 128 × 128 pixels (Fig. 3(a)). Fig. 3(b) shows the result using the HE deployed with the full RGB color space. Here, the abnormal region has been obscured and the colors are distorted in the normal regions. After applying the proposed method, the abnormal region in Fig. 3(c) is more clearly visualized in the enhanced image, which also preserves the natural color tones around the abnormal regions. Image-Enhanced Capsule Endoscopy Preserving the Original Color Tones (a) (b) 39 (c) Fig.

Histogram equalization is applied to the ROI’s image data in the proposed GI color space. Consequently, by merging the enhanced version of the ROI with the original image, the proposed method generates a full enhanced image that preserves the natural tones. 2 Constructing the GI Color Space The GI color space was first mentioned in [15]. g, areas including dark lumen, water bubbles, gas, Image-Enhanced Capsule Endoscopy Preserving the Original Color Tones 37 (a) (a) (b) (c) Fig. 1. Color components classification.

C) Final subtracted result. 24 N. Panjwani et al. Fig. 4. The original CT (A) from Figure 2A and the result of our stool removal algorithm (B) Fig. 5. CT images with polyps (green circles) before (A and C) and after (B and D) cleansing. The CAD system was run on the CTC scans with and without the new module for stool subtraction. The comparative FROC curves are presented in Fig. 7. As expected, the automatic detection of polyps is hampered by the presence of stool in the scans, as heterogeneous stool represents a prevailing source of false positives (FP).

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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.)


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