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Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network

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Abstract

Wireless Capsule Endoscopy (WCE), which allows clinicians to inspect the whole gastrointestinal tract (GI) noninvasively, has bloomed into one of the most efficient technologies to diagnose the bleeding in GI tract. However WCE generates large amount of images in one examination of a patient. It is hard for clinicians to leave continuous time to examine the full WCE images, and this is the main factor limiting the wider application of WCE in clinic. A novel intelligent bleeding detection based on Probabilistic Neural Network (PNN) is proposed in this paper. The features of bleeding region in WCE images distinguishing from non-bleeding region are extracted. A PNN classifier is built to recognize bleeding regions in WCE images. Finally the intelligent bleeding detection method is implemented through programming. The experiments show this method can correctly recognize the bleeding regions in WCE images and clearly mark them out. The sensitivity and specificity on image level are measured as 93.1% and 85.6% respectively.

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Acknowledgement

This research was supported by the National Hi-Tech Research and Development Program (863) of China (2006AA04Z368) and the National Natural Science Foundation of China (30570485).

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Authors and Affiliations

  1. School of Electronics, Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai, 200240, People’s Republic of China

    Guobing Pan, Guozheng Yan, Xiangling Qiu & Jiehao Cui

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  1. Guobing Pan
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  2. Guozheng Yan
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  3. Xiangling Qiu
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  4. Jiehao Cui
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Correspondence to Guobing Pan.

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Pan, G., Yan, G., Qiu, X. et al. Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network. J Med Syst 35, 1477–1484 (2011). https://doi.org/10.1007/s10916-009-9424-0

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  • DOI: https://doi.org/10.1007/s10916-009-9424-0

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