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International Journal of Biomedical Imaging
Volume 2015, Article ID 109804, 11 pages
http://dx.doi.org/10.1155/2015/109804
Research Article

Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network

1Department of Computer Science, Chubu University, 1200 Matsumotocho, Kasugai 487-8501, Japan
2Department of Electronics and Electrical Engineering, IIT Guwahati, Guwahati 781039, India
3Department of Computer Science, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
4Department of Gastroenterology, Aichi Medical University, 1-1 Karimata, Yazako, Nagakute 480-1195, Japan

Received 31 October 2014; Accepted 10 March 2015

Academic Editor: Richard H. Bayford

Copyright © 2015 Seiya Tsuda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Medical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgment. As a method to recover 3D shape with high speed, VBW (Vogel-Breuß-Weickert) model is proposed to recover 3D shape under the condition of point light source illumination and perspective projection. However, VBW model recovers the relative shape but there is a problem that the shape cannot be recovered with the exact size. Here, shape modification is introduced to recover the exact shape with modification from that with VBW model. RBF-NN is introduced for the mapping between input and output. Input is given as the output of gradient parameters of VBW model for the generated sphere. Output is given as the true gradient parameters of true values of the generated sphere. Learning mapping with NN can modify the gradient and the depth can be recovered according to the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment.