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Recovering Polyp Shape from an Endoscope Image Using Two Light Sources

Recovering Polyp Shape from an Endoscope Image Using Two Light Sources

Hiroyasu Usami, Yuji Iwahori, Yuki Hanai, Boonserm Kijsirikul, Kunio Kasugai
Copyright: © 2017 |Volume: 5 |Issue: 2 |Pages: 22
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781522515548|DOI: 10.4018/IJSI.2017040103
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MLA

Usami, Hiroyasu, et al. "Recovering Polyp Shape from an Endoscope Image Using Two Light Sources." IJSI vol.5, no.2 2017: pp.33-54. http://doi.org/10.4018/IJSI.2017040103

APA

Usami, H., Iwahori, Y., Hanai, Y., Kijsirikul, B., & Kasugai, K. (2017). Recovering Polyp Shape from an Endoscope Image Using Two Light Sources. International Journal of Software Innovation (IJSI), 5(2), 33-54. http://doi.org/10.4018/IJSI.2017040103

Chicago

Usami, Hiroyasu, et al. "Recovering Polyp Shape from an Endoscope Image Using Two Light Sources," International Journal of Software Innovation (IJSI) 5, no.2: 33-54. http://doi.org/10.4018/IJSI.2017040103

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Abstract

This paper proposes a new approach to recover the polyp shape from an endoscope image using a photometric constraint equation considering two light sources. The procedures are as follows. First, obtain the initial depth distributions by optimizing photometric equation obtained from two light sources. Next, obtain the surface normal vector from depth using numerical difference at each point. Then the mapping between the obtained normal vector and true normal vector is learned using Radial Basis Function Neural Network for a Lambertian sphere, and learning is generalized to another actual polyp image. Finally, optimize the depth using the obtained surface normal to recover the final 3D shape. The validity is confirmed of this method in comparison with the previous methods via computer simulation and experiments using actual endoscope images.

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