Research on a New Feature Detection Algorithm for Wireless Capsule Endoscope Bleeding Images Based on Superpixel Segmentation Gastric hemorrhage is the manifestation of many digestive tract diseases. The traditional gastroscope detection has the disadvantages of insecurity, discomfort and unsanitary. As a new type of detection technology, wireless capsule endoscope (WCE) overcomes the defects of traditional gastroscope detection. But in the process of detection, due to the frequency of 2 frames per second, a huge amount of pictures will be generated. It is unreasonable to rely on doctors to manually screen images of gastric bleeding from massive pictures. Therefore, there is an urgent need for a high standard of computeraided medical technology. Based on this purpose, our paper proposes a new detection feature, the RG coefficient of variation of superpixel block, which is used to automatically detect gastric hemorrhage. Firstly, the superpixel segmentation method SLIC is used to segment the images, which preserves the homogeneity and edge information of the pixels in the region. Secondly, the new detection features are extracted from the super pixel blocks, compared to the traditional color features and the currently widely used color detection features. Kfold cross validation is performed on our data set. Finally, by using the new detection features proposed in our paper, the accuracy of the detection is improved by 4.13%, the sensitivity is improved by 7.5% and the miss detection rate is actually reduced. Achieve the automatic screening of suspicious bleeding images under the condition of ensuring high standards, and reduce the diagnosis time.

MultiStage AttentionUnet for Wireless Capsule Endoscopy Image Bleeding Area Segmentation Bleeding in gastrointestinal tract (GI) is caused by many different diseases and may lead to serious consequence if not being treated correctly. The most effective method to locate bleeding area is using wireless capsule endoscopy (WCE). However, the amount of WCE images of one patient is so large that even a professional physician may take a long time on analyzing them, and human errors may also occur with work time increasing. Thus the computeraided diagnosis methods become more attractive. In this paper, we propose a novel deep learning based method for WCE images bleeding area segmentation. We design multistage architecture and attention blocks to deal with small areas segmentation. The latter stagesu0027 input is the combination of feature maps transferred from former stages so that even very deep layers can obtain small areas features, and attention blocks help shallow layers to better extract small areas features thus they can transfer more useful information to latter stages. Extensive experiments are conducted on public available WCE image dataset to show the effect of multistage architecture and attention blocks. Compared with other bleeding areas segmentation methods, our approach achieves stateoftheart performance with 98.5% overall accuracy, 90.1% mean accuracy and 86.3% mean intersection over union (IoU). There is a 10.7% improvement on mean IoU compared with the previous stateoftheart WCE bleeding segmentation method.