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Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding

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Computational Intelligence in Data Mining—Volume 1

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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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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Authors and Affiliations

  1. Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA

    V. B. Surya Prasath

  2. Knowledge Based System Group, Higher Institute for Information Technology and Information Systems, Kazan Federal University, Kazan, Russia

    Radhakrishnan Delhibabu

Authors
  1. V. B. Surya Prasath
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  2. Radhakrishnan Delhibabu
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Corresponding author

Correspondence to V. B. Surya Prasath .

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Editors and Affiliations

  1. Veer Surendra Sai University of Tech., Dept of Computer Sci Eng & IT, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. National Institute of Technology, Dept. of Computer Science & Engineering, Rourkela, Odisha, India

    Durga Prasad Mohapatra

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Surya Prasath, V.B., Delhibabu, R. (2016). Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_10

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  • DOI: https://doi.org/10.1007/978-81-322-2734-2_10

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2732-8

  • Online ISBN: 978-81-322-2734-2

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