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Modification of Polyp Size and Shape from Two Endoscope Images Using RBF Neural Network

  • Yuji IwahoriEmail author
  • Seiya Tsuda
  • Robert J. Woodham
  • M. K. Bhuyan
  • Kunio Kasugai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9493)

Abstract

In the medical imaging applications, endoscope image is used to observe the human body. The VBW (Vogel-Breuß-Weickert) model is proposed as a method to recover 3-D shape under point light source illumination and perspective projection. However, the VBW model recovers relative, not absolute, shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images obtained with movement of the endoscope camera in depth. A Lambertian sphere image is generated using the estimated reflectance parameter and VBW model is applied for a sphere. Then Radial Basis Function Neural Network (RBF-NN) learning is applied. The NN implements the shape modification. NN input is the gradient parameters produced by the VBW model for the generated sphere. NN output is the true gradient parameters for the true values of the generated sphere. Here, regression analysis is introduced in comparison with the performance by NN. Depth can then be recovered using the modified gradient parameters. Performance of shape modification by NN and regression analysis was evaluated via computer simulation and real experiment. The result suggests that NN gives better performance than the regression analysis to improve the absolute size and shape of polyp.

Keywords

Endoscope image VBW model RBF-NN Regression analysis Shape modification Reflection factor 

Notes

Acknowledgement

Iwahori’s research is supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) (26330210) and Chubu University Grant. Woodham’s research is supported by the Natural Sciences and Engineering Research Council (NSERC). The authors would like to thank Kodai Inaba for his experimental help and the related member for useful discussions in this paper.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yuji Iwahori
    • 1
    Email author
  • Seiya Tsuda
    • 1
  • Robert J. Woodham
    • 2
  • M. K. Bhuyan
    • 3
  • Kunio Kasugai
    • 4
  1. 1.Faculty of EngineeringChubu UniversityKasugaiJapan
  2. 2.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada
  3. 3.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  4. 4.Department of GastroenterologyAichi Medical UniversityNagakute-cho, Aichi-gunJapan

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