Bleeding detection in Wireless Capsule Endoscopy based on Probabilistic Neural Network.
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.
Pan
Guobing
G
School of Electronics, Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai, People's Republic of China. Guobpan@gmail.com
Yan
Guozheng
G
Qiu
Xiangling
X
Cui
Jiehao
J
D053704:Capsule Endoscopy / Q000009:adverse effects*; D006470:Hemorrhage / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D016571:Neural Networks, Computer; D011336:Probability; D012680:Sensitivity and Specificity