Fast Reducing Redundancy in Capsule Endoscopy Video Based on SURF
Liu Xiaoyan1*, Gong Junhui1, 3, Li Xiangdong2, Wu Weining1, Peng Tongsheng1
1(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China) 2(Suzhou OIH Medical Scientific LTD. CO., Suzhou 215000, Jiangsu, China) 3(College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411101, Hunan,China)
Abstract：Capsule endoscopy captures tens of thousands of images through the whole digestive system with a large number of redundant images. To determine abnormal images would consume considerable time and effort of medical doctors by checking each frame. Therefore, it is meaningful to screen out the redundant images automatically. In this paper, feature points and the corresponding feature vector was extracted in successive capsule endoscopy frames with SURF. Vector angular was applied to evaluate the matching degree between two feature points from the successive frames. Maximum between-class variance was used to identify the matched feature points adaptively. Finally, the quantity of matched feature points and block matching degree was used as the index to screen out redundant images. Experimental tests in captured 128 videos by capsule endoscopy demonstrate that the proposed approach has a rapid processing speed of 0.06 seconds/frame, with good performance in the recall, precision and F-measure index (81%, 90%, and 85%, respectively). Moreover, it shows good robustness to uncertainties such as peristalsis, illumination and bubbles.
刘小燕, 龚军辉, 李向东, 武伟宁, 彭同胜. 基于SURF算法的胶囊内镜冗余图像快速筛除[J]. 中国生物医学工程学报, 2016, 35(3): 264-271.
Liu Xiaoyan, Gong Junhui, Li Xiangdong, Wu Weining, Peng Tongsheng. Fast Reducing Redundancy in Capsule Endoscopy Video Based on SURF. Chinese Journal of Biomedical Engineering, 2016, 35(3): 264-271.
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