A Smart Capsule System for Automated Detection of Intestinal Bleeding Using HSL Color Recognition

Panpan Qiao; Hongying Liu; Xueping Yan; Ziru Jia; Xitian Pi

doi:10.1371/journal.pone.0166488

A Smart Capsule System for Automated Detection of Intestinal Bleeding Using HSL Color Recognition

PLoS One. 2016 Nov 30;11(11):e0166488. doi: 10.1371/journal.pone.0166488. eCollection 2016.

Authors

Panpan Qiao¹, Hongying Liu^{1

2}, Xueping Yan¹, Ziru Jia¹, Xitian Pi^{1

3}

Affiliations

¹ Key Laboratory of Biorheological Science and Technology of Ministry of Education, Bioengineering College, Chongqing University, Chongqing, PR China.
² Chongqing Engineering Research Center of Medical Electronics, Chongqing, PR China.
³ Key Laboratories for National Defense Science and Technology of innovative micro-nano devices and system technology, Chongqing University, Chongqing, PR China.

Abstract

There are no ideal means for the diagnosis of intestinal bleeding diseases as of now, particularly in the small intestine. This study investigated an intelligent intestinal bleeding detection capsule system based on color recognition. After the capsule is swallowed, the bleeding detection module (containing a color-sensitive adsorptive film that changes color when absorbing intestinal juice,) is used to identify intestinal bleeding features. A hue-saturation-light color space method can be applied to detect bleeding according to the range of H and S values of the film color. Once bleeding features are recognized, a wireless transmission module is activated immediately to send an alarm signal to the outside; an in vitro module receives the signal and sends an alarm. The average power consumption of the entire capsule system is estimated to be about 2.1mW. Owing to its simplicity, reliability, and effectiveness, this system represents a new approach to the clinical diagnosis of intestinal bleeding diseases.

MeSH terms

Algorithms
Capsule Endoscopy / methods*
Color
Gastrointestinal Hemorrhage / diagnosis*
Gastrointestinal Hemorrhage / diagnostic imaging*
Humans
Image Processing, Computer-Assisted / methods*
Intestinal Diseases / diagnosis
Intestinal Diseases / diagnostic imaging
Intestine, Small / blood supply
Intestine, Small / pathology*
Pattern Recognition, Automated / methods*

Grants and funding

This work was supported by National Key Technology Support Program (2015BAI01B13), http://program.most.gov.cn/; The Medical Instrument Key Project of Chongqing (No. cstc2015zdcy-ztzx10002), http://www.cstc.gov.cn/; National Natural Science Foundation of China(81671850, http://www.nsfc.gov.cn/; and the Fundamental Research Funds for the Central Universities (106112016CDJXY230004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.