Abstract
The diagnostic yield of colon cancer screening using colonoscopy could improve using intelligent systems. The large amount of data provided by high definition equipments contains frames with large non-informative regions. Non-informative regions have such a low visual quality that even physicians can not properly identify structures. Thus, identification of such regions is an important step for an efficient and accurate processing. We present a strategy for discarding non-informative regions in colonoscopy frames based on a model of appearance of such regions. Three different methods are proposed to characterize accurately the boundary between informative and non-informative regions. Preliminary results shows that there is a statistically significant difference between each of the methods as some of them are more strict when deciding which part of the image is informative and others regarding which is the non-informative region.
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Acknowledgments
This work was supported by a research grant from Universitat Autónoma de Barcelona 471-01- 2/2010 and by Spanish projects \(TIN2009-10435\), \(TIN2009-13618\) and \(TIN2012-33116\).
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Bernal, J., Gil, D., Sánchez, C., Sánchez, F.J. (2014). Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2014. Lecture Notes in Computer Science(), vol 8899. Springer, Cham. https://doi.org/10.1007/978-3-319-13410-9_1
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DOI: https://doi.org/10.1007/978-3-319-13410-9_1
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