Wireless capsule endoscopy (WCE) offers a feasible noninvasive way to detect the whole gastrointestinal (GI) tract and revolutionizes the diagnosis technology. However, compared with wired endoscopies, the limited working time, the low frame rate, and the low image resolution limit the wider application. The progress of this new technology is reviewed in this paper, and the evolution tendencies are analyzed to be high image resolution, high frame rate, and long working time. Unfortunately, the power supply of capsule endoscope (CE) is the bottleneck. Wireless power transmission (WPT) is the promising solution to this problem, but is also the technical challenge. Active CE is another tendency and will be the next geneion of the WCE. Nevertheless, it will not come true shortly, unless the practical locomotion mechanism of the active CE in GI tract is achieved. The locomotion mechanism is the other technical challenge, besides the challenge of WPT. The progress about the WPT and the active capsule technology is reviewed.
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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