Abstract
Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.
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References
Iddan, G., Meron, G., Glukhovsky, A., and Swain, P., Wireless capsule endoscopy. Nature. 405:417, 2000. doi:10.1038/35013140.
Hassan, A.R., and Haque, M.A., Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Programs Biomed. 122:341–353, 2015. doi:10.1016/j.cmpb.2015.09.005.
Sainju, S., Bui, F.M., and Wahid, K.A., Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J Med Syst. 38:25, 2014. doi:10.1007/s10916-014-0025-1.
Ghosh, T., Fattah, S.A., Shahnaz, C., et al., An automatic bleeding detection scheme in wireless capsule endoscopy based on histogram of an RGB-indexed image. Conf Proc IEEE Eng Med Biol Soc. 2014:4683–4686, 2014. doi:10.1109/EMBC.2014.6944669.
Lv, G., Yan, G., and Wang, Z., Bleeding detection in wireless capsule endoscopy images based on color invariants and spatial pyramids using support vector machines. Conf Proc IEEE Eng Med Biol Soc. 2011:6643–6646, 2011. doi:10.1109/IEMBS.2011.6091638.
Giritharan, B., Yuan, X., Liu, J., et al., Bleeding detection from capsule endoscopy videos. Conf Proc IEEE Eng Med Biol Soc. 2008:4780–4783, 2008. doi:10.1109/IEMBS.2008.4650282.
Lau, P.Y., and Correia, P.L., Detection of bleeding patterns in WCE video using multiple features. Conf Proc IEEE Eng Med Biol Soc. 2007:5601–5604, 2007. doi:10.1109/IEMBS.2007.4353616.
Iakovidis, D.K., and Koulaouzidis, A., Automatic lesion detection in capsule endoscopy based on color saliency: Closer to an essential adjunct for reviewing software. Gastrointest Endosc. 80:877–883, 2014. doi:10.1016/j.gie.2014.06.026.
Karargyris, A., and Bourbakis, N., Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans Biomed Eng. 58:2777–2786, 2011. doi:10.1109/TBME.2011.2155064.
Rokkas, T., Papaxoinis, K., Triantafyllou, K., et al., Does purgative preparation influence the diagnostic yield of small bowel video capsule endoscopy?: A meta-analysis. Am J Gastroenterol. 104:219–227, 2009. doi:10.1038/ajg.2008.63.
Endo, H., Kondo, Y., Inamori, M., et al., Ingesting 500 ml of polyethylene glycol solution during capsule endoscopy improves the image quality and completion rate to the cecum. Dig Dis Sci. 53:3201–3205, 2008. doi:10.1007/s10620-008-0292-0.
Niv, Y., Niv, G., Wiser, K., and Demarco, D.C., Capsule endoscopy - comparison of two strategies of bowel preparation. Aliment Pharmacol Ther. 22:957–962, 2005. doi:10.1111/j.1365-2036.2005.02647.x.
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. John Wiley & Sons; New York, pp 20-41.
Van Weyenberg, S.J., De Leest, H.T., and Mulder, C.J., Description of a novel grading system to assess the quality of bowel preparation in video capsule endoscopy. Endoscopy. 43:406–411, 2011. doi:10.1055/s-0030-1256228.
Acknowledgements
We thank Dr. Tatsuaki Etou and Kensuke Yamada for his expert technical assistance in running the computer program. We also acknowledge Editage (www.editage.jp) for English language editing.
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Hashimoto, S., Ogihara, H., Suenaga, M. et al. An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images. J Med Syst 41, 119 (2017). https://doi.org/10.1007/s10916-017-0769-5
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DOI: https://doi.org/10.1007/s10916-017-0769-5