TY - GEN
T1 - Novel Wireless Capsule Endoscopy diagnosis system with adaptive image capturing rate
AU - Jin, Zhi
AU - Tillo, Tammam
AU - Lim, Eng Gee
AU - Wang, Zhao
AU - Xiao, Jimin
PY - 2013
Y1 - 2013
N2 - Wireless Capsule Endoscopy (WCE) is a device used to diagnose the gastrointestinal (GI) track, and it is one of the most used tools to inspect the small intestine. Inspection by WCE is non-invasive, and consequently it is more popular if compared to other methods that are traditionally adopted in the examination of GI track. From the point of view of the physicians, WCE is a favorable approach in increasing both the efficiency and the accuracy of the diagnosis. The most significant drawback of WCE is the time consumption for a physician to check all the frames taken in the GI track, in fact it is too long, and could be up to 4 hours. Many anomaly-based techniques were proposed to help physician shorten the diagnosis time, however, these techniques still suffer from high false alarm rate, which limits their actual use. Therefore, in this paper we propose a two stage diagnosis system that firstly uses a normal capsule to capture the whole GI track, and then we use an automatic detection technique that detects anomalies with high false alarm rate. The low specificity of the first capsule ensures that no anomalies will be missed in the first stage of the process. The second stage of the proposed diagnosis system uses a different capsule with adaptive image capturing rate to re-capture the GI tract. In this stage the capsule will use high image capturing rate for segments of GI tract where an anomaly was detected in the first stage, whereas, in the other segments of the GI tract a lower image capturing rate will be used in order to have better use of the second capsule's battery. Consequently, the second generated video, which will be inspected by the physician, will have higher resolution sequence around the areas with suspected lesion.
AB - Wireless Capsule Endoscopy (WCE) is a device used to diagnose the gastrointestinal (GI) track, and it is one of the most used tools to inspect the small intestine. Inspection by WCE is non-invasive, and consequently it is more popular if compared to other methods that are traditionally adopted in the examination of GI track. From the point of view of the physicians, WCE is a favorable approach in increasing both the efficiency and the accuracy of the diagnosis. The most significant drawback of WCE is the time consumption for a physician to check all the frames taken in the GI track, in fact it is too long, and could be up to 4 hours. Many anomaly-based techniques were proposed to help physician shorten the diagnosis time, however, these techniques still suffer from high false alarm rate, which limits their actual use. Therefore, in this paper we propose a two stage diagnosis system that firstly uses a normal capsule to capture the whole GI track, and then we use an automatic detection technique that detects anomalies with high false alarm rate. The low specificity of the first capsule ensures that no anomalies will be missed in the first stage of the process. The second stage of the proposed diagnosis system uses a different capsule with adaptive image capturing rate to re-capture the GI tract. In this stage the capsule will use high image capturing rate for segments of GI tract where an anomaly was detected in the first stage, whereas, in the other segments of the GI tract a lower image capturing rate will be used in order to have better use of the second capsule's battery. Consequently, the second generated video, which will be inspected by the physician, will have higher resolution sequence around the areas with suspected lesion.
KW - Image recognition technique
KW - Smart image capturing rate
KW - Wireless Capsule Endoscopy
UR - http://www.scopus.com/inward/record.url?scp=84878257510&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:84878257510
SN - 9789898565471
T3 - VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
SP - 143
EP - 147
BT - VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
T2 - 8th International Conference on Computer Vision Theory and Applications, VISAPP 2013
Y2 - 21 February 2013 through 24 February 2013
ER -