National Science and Technology Support Program [2012BAI14B03]
; NSFC [61105013, 61375014, 61533015]
; Foundation of Chinese Scholarship Council
摘要:
Objective: This paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video. Methods and materials: To select the most representative frames from the original video sequence, we formulate the problem of gastroscopic video summarization as a dictionary selection issue. Different from the traditional dictionary selection methods, which take into account only the number and reconstruction ability of selected key frames, our model introduces the similar-inhibition constraint to reinforce the diversity of selected key frames. We calculate the attention cost by merging both gaze and content change into a prior cue to help select the frames with more high-level semantic information. Moreover, we adopt an image quality evaluation process to eliminate the interference of the poor quality images and a segmentation process to reduce the computational complexity. Results: For experiments, we build a new gastroscopic video dataset captured from 30 volunteers with more than 400k images and compare our method with the state-of-the-arts using the content consistency, index consistency and content-index consistency with the ground truth. Compared with all competitors, our method obtains the best results in 23 of 30 videos evaluated based on content consistency, 24 of 30 videos evaluated based on index consistency and all videos evaluated based on content-index consistency. Conclusions: For gastroscopic video summarization, we propose an automated annotation method via similar-inhibition dictionary selection. Our model can achieve better performance compared with other state-of-the-art models and supplies more suitable key frames for diagnosis. The developed algorithm can be automatically adapted to various real applications, such as the training of young clinicians, computer aided diagnosis or medical report generation. (C) 2015 Elsevier B.V. All rights reserved.
语种:
英语
WOS标题词:
Science & Technology
; Technology
; Life Sciences & Biomedicine
类目[WOS]:
Computer Science, Artificial Intelligence
; Engineering, Biomedical
; Medical Informatics
1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang, China 2.Department of Computer Science, Arizona State University, 1711 South Rural Road, Tempe, AZ, United States 3.Key Laboratory of Image Understanding and Computer Vision, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang, China 4.University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China 5.Department of Gastroenterology and Hepatology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, China 6.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang, China
Recommended Citation:
Wang S,Cong Y,Cao, Jun,et al. Scalable gastroscopic video summarization via similar-inhibition dictionary selection[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2016,66:1-13.
