Algorithms for Automatic Recognition of Non-informative Frames in Video Recordings of Bronchoscopic Procedures
The video recordings of endoscopic procedures performed within respiratory tract include both frames of adequate and inadequate quality for the assessment by the endoscopist. The frames of inadequate quality were called by some authors blurred or “non-informative”. The fraction of blurred frames within video recording of bronchofiberoscopy may be considerable and it varies from case to case. Therefore, the function of automatic exclusion of “non-informative” frames would bring substantial benefits in terms of the volume of the archived video recordings of bronchofiberoscopic procedures. Furthermore, it could also save the time of users accessing medical video library established with archived resources. In this paper, the authors have proposed, tested and compared several algorithms for detecting blurred video frames. The main focus of this paper is to compare various, independent algorithms for automatic recognition of “non-informative” frames in video recordings of bronchoscopic procedures. The results demonstrated in the paper show that the proposed methods achieve F-measure, sensitivity, specificity and accuracy of at least 87% or higher.
KeywordsDiscrete Cosine Transform Video Recording Capsule Endoscopy Automatic Recognition Capsule Endoscopy Video
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- 1.Hwang, S., Oh, J., Lee, J., Cao, Y., Tavanapong, W., Liu, D., Wong, J., de Groen, P.C.: Automatic measurement of quality metrics for colonoscopy videos. In: MULTIMEDIA 2005: Proceedings of the 13th annual ACM International Conference on Multimedia, pp. 912–921. ACM, New York (2005)CrossRefGoogle Scholar
- 2.Hwang, S., Oh, J., Lee, J.: Informative frames classificatin for endoscopy video. Medical Image Analysis 11(2), 100–127 (2007)Google Scholar
- 3.Jozwiak, R., Przelaskowski, A., Duplaga, M.: Diagnostically useful video content extraction for integrated computer-aided bronchoscopy examination system. In: CORES 2009 (2009)Google Scholar
- 4.Vilarino, F., Spyridonos, P.: Automatic detection of intestinal juices in wireless capsule video endoscopy. In: 18th International Conference on Pattern Recognition (2006)Google Scholar
- 6.Karkanis, S., Iakovidis, D., Karras, D., Maroulis, D.: Detection of lesions in endoscopic video using textural descriptors on wavelet domain supported by artificial neural network architectures. In: IEEE ICIP, pp. 833–836 (2001)Google Scholar
- 8.Kodogiannis, V., Lygouras, J.: A computerised diagnostic decision support system in wireless-capsule endoscopy. In: 3rd International IEEE Conference Intelligent Systems, pp. 638–644 (2006)Google Scholar
- 9.Lee, J., Oh, J., Shah, S., Yuan, X., Tang, S.: Automatic classification of digestive organs in wireless capsule endoscopy videos. In: Proceedings of the 2007 ACM Symposium on Applied Computing, pp. 1041–1045 (2007)Google Scholar
- 10.Cao, Y., Liu, D., Tavanapong, W.: Automatic classification of images with appendiceal orifice in colonoscopy videos. In: 28th IEEE EMBS Annual International Conference, pp. 2349–2352 (2006)Google Scholar
- 11.ISO/IEC: Information technology – multimedia content description interface. ISO/IEC 15938 (2002)Google Scholar
- 12.Grega, M., Leszczuk, M.: The prototype software for video summarization of bronchoscopy procedures with the use of mechanisms designed to identify, index and search. Paper submitted for 2nd International Conference on Information Technologies in Biomedicine (2010)Google Scholar