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On the Antecedent Sets for Fuzzy Classification of Colorectal Polyps with Stabilized KH Interpolation

  • Szilvia Nagy
  • Ferenc Lilik
  • Laszlo T. Koczy
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 796)

Abstract

Polyps in the colorectal part of the bowel appear often, and in many cases these polyps can develop into malign lesions, such as cancer. Colonoscopy is the most efficient way to study the inner surface of the colorectum, and doctors usually are able to detect polyps on a motion picture diagnostic session. However, it is useful to have an automated tool that can help drawing attention to given parts of the image, and later a method for classification the polyps can also be developed. Statistical properties of the colour channels of the images are used as antecedents for a fuzzy decision system, together with edge densities and Renyi entropies-based structural entropy. However promising the processed images are, the variation in the preparation of the diagnosis as well as the practice of the operating personnel can lead to images with significantly different noise and distortion level, thus detecting the polyp can be complicated. In the following considerations image groups are presented that have similarities from the polyp detection point of view, and those type of images are also given, which can spoil a well prepared detecting system.

Keywords

Fuzzy inference Colorectal polyp Fuzzy rule interpolation Image segmentation 

Notes

Acknowledgements

The authors would like to thank the financial support of the project EFOP-3.6.2-16-2017-00015 HU MATHS—IN—Intensification of the activity of the Hungarian Industrial Innovation Mathematical Service Network, and the ÚNKP-17-4-III-SZE-16 New National Excellence Programme of the Ministry of Human Capacities of Hungary.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Szechenyi Istvan UniversityGyőrHungary
  2. 2.Budapest University of Technology and EconomicsBudapestHungary

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