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Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)

Abstract

Video capsule endoscopy (VCE) is a revolutionary imaging technique widely used in visualizing the gastrointestinal tract. The amount of big data generated by VCE necessitates automatic computed aided-diagnosis (CAD) systems to aid the experts in making clinically relevant decisions. In this work, we consider an automatic tissue detection method that uses an adaptive entropy thresholding for better separation of mucosa which lines the colon wall from lumen, which is the hollowed gastrointestinal tract. Comparison with other thresholding methods such as Niblack, Bernsen, Otsu, and Sauvola as well as active contour is undertaken. Experimental results indicate that our method performs better than other methods in terms of segmentation accuracy in various VCE videos.

Keywords

Segmentation Video capsule Endoscopy Mucosa detection Thresholding 

Notes

Acknowledgments

This work was funded by the subsidy of the Russian Government to support the Program of competitive growth of Kazan Federal University among world class academic centers and universities.

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.Knowledge Based System Group, Higher Institute for Information Technology and Information SystemsKazan Federal UniversityKazanRussia

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