Automatic Detection of Salient Video Frames from Wireless Capsule Endoscopy using Adaptive Singular Value Decomposition

Biniaz, Abbas; Abdolali, Fatemeh; Aghaeizadeh Zoroofi, Reza; Haji Maghsoudi, Omid; Sato, Yoshinobu

doi:10.22041/ijbme.2019.94378.1406

Document Type : Full Research Paper

Authors

¹ Ph.D. Student, Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

² Graduated Ph.D. Student, Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

³ Professor, Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

⁴ Ph.D. Student, Department of Bioengineering, Temple University, Philadelphia, USA, PA 19144

⁵ Professor, Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan

https://doi.org/10.22041/ijbme.2019.94378.1406

Abstract

Wireless capsule endoscopy is a non-invasive diagnosis method which allows recording a video as the capsule travels through the gastrointestinal tract. The practical drawback is producing a long clinical video up to 8 hours and it takes about 2 hours to review the exam by an experienced expert. Video summarization methods can reduce the time required by experts and errors in manual interpretation. This paper presents an automatic method based on unique properties of adaptive singular value decomposition through sliding window that can reduce the long annotation time. By utilizing these properties, we are able to summarize a WCE video by outputting a motion video summary. Moreover, we apply an effective approach based on adaptive contrast diffusion to correct uneven illumination that deal with the low contrast generally caused by poor visibility conditions of the GI tract, WCE power and its structure. Experimental results on WCE videos indicate that a significant reduction in the review time is feasible. Quantitative and qualitative results of summarization show the effectiveness of proposed method that can be adapted to various clinical applications, such as training of young physicians, computer assisted diagnosis, medical decision support or medical document management.

Keywords

Main Subjects

Biomedical Image Processing / Medical Image Processing

References

[1] S. P. Rajendra and N. Keshaveni, “A Survey of Automatic Video Summarization Techniques,” Int. J. Electron. Comput. Syst., vol. 3, no. 1, pp. 1–6, 2014.

[2] A. Mohammed, S. Yildirim, M. Pedersen, O. Hovde, and F. Cheikh, “Sparse Coded Handcrafted and Deep Features for Colon Capsule Video Summarization,” Proc. - IEEE Symp. Comput. Med. Syst., vol. 2017–June, pp. 728–733, 2017.

[3] S. Seguí et al., “Generic feature learning for wireless capsule endoscopy analysis,” Comput. Biol. Med., vol. 79, pp. 163–172, 2016.

[4] D. K. Iakovidis and A. Koulaouzidis, “Software for enhanced video capsule endoscopy: Challenges for essential progress,” Nat. Rev. Gastroenterol. Hepatol., vol. 12, no. 3, pp. 172–186, 2015.

[5] M.H. A. Muhammad Ajmal Muhammad Shakir and and F. A. S. Yasir Abbas, “Video Summarization Techniques and Classification,” in Computer Vision and Graphics. ICCVG, 2012, pp. 1–13.

[6] S. Eliza, F. De Avila, A. Paula, B. Lopes, L. Jr, and A. De Albuquerque, “VSUMM : A mechanism designed to produce static video summaries and a novel evaluation method,” Pattern Recognit. Lett., vol. 32, no. 1, pp. 56–68, 2011.

[7] S. Zhang, Y. Zhu, and A. K. Roy-Chowdhury, “Context-Aware Surveillance Video Summarization,” IEEE Trans. Image Process., vol. 25, no. 11, pp. 5469–5478, 2016.

[8] S. Wang et al., “Scalable gastroscopic video summarization via similar-inhibition dictionary selection,” Artif. Intell. Med., vol. 66, pp. 1–13, 2016.

[9] M. M. Ben Ismail, O. Bchir, and A. Z. Emam, “Endoscopy Video Summarization based on Unsupervised Learning and Feature Discrimination,” in Visual Communications and Image Processing (VCIP), 2013, pp. 1–6.

[10] D. K. Iakovidis, S. Tsevas, and A. Polydorou, “Reduction of capsule endoscopy reading times by unsupervised image mining,” Comput. Med. Imaging Graph., vol. 34, no. 6, pp. 471–478, 2010.

[11] K. Muhammad, M. Sajjad, M. Young, and S. Wook, “Efficient visual attention driven framework for key frames extraction from hysteroscopy videos,” Biomed. Signal Process. Control, vol. 33, pp. 161–168, 2017.

[12] I. Mehmood, M. Sajjad, and S. W. Baik, “Video summarization based tele-endoscopy : a service to efficiently manage visual data generated during wireless capsule endoscopy procedure,” J. Med. Syst., 2014.

[13] A. Koulaouzidis and D. K. Iakovidis, “KID: A capsule endoscopy database for medical decision support,” United Eur. Gastroenterol. Week (UEGW). Barcelona, Spain, 2015.

[14] “kid.” [Online]. Available: https: // mdss.uth.gr /datasets/endoscopy/kid/.

[15] A. Vadivel, S. Sural, and A. K. Majumdar, “Human color perception in the HSV space and its application in histogram generation for image retrieval.,” in Color Imaging: Processing, Hardcopy, and Applications, 2005, pp. 598–609.

[16] Y. Gong and X. Liu, “Video summarization and retrieval using singular value decomposition,” Multimed. Syst., vol. 168, pp. 157–168, 2003.

[17] R. Badeau, G. Richard, and B. David, “Sliding window adaptive SVD algorithms,” IEEE Trans. Signal Process., vol. 52, no. 1, pp. 1–10, 2004.

[18] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis,” J. Am. Soc. Inf. Sci., vol. 41, no. 6, p. 391, 1990.

[19] E. Anderson et al., “LAPACK: A portable linear algebra library for high-performance computers,” in Proceedings of the 1990 ACM/IEEE conference on Supercomputing, 1990, pp. 2–11.

[20] G. H. Golub and C. F. Van Loan, Matrix computations, vol. 3. JHU Press, 2012.

[21] Y. Gong and X. Liu, “Video summarization using singular value decomposition,” in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, 2000, vol. 2, pp. 174–180.

[22] B. Li and M. Q.-H. Meng, “Wireless capsule endoscopy images enhancement via adaptive contrast diffusion,” J. Vis. Commun. Image Represent., vol. 23, no. 1, pp. 222–228, 2012.

[23] G.-H. Cottet and M. El Ayyadi, “A Volterra type model for image processing,” IEEE Trans. image Process., vol. 7, no. 3, pp. 292–303, 1998.

[24] A. Biniaz and A. Abbasi, “Unsupervised ACO : Applying FCM as a supervisor for ACO in medical image segmentation,” J. Intell. Fuzzy Syst., vol. 27, pp. 407–417, 2014.

Iranian Journal of Biomedical Engineering

Automatic Detection of Salient Video Frames from Wireless Capsule Endoscopy using Adaptive Singular Value Decomposition

References

References

Volume 12, Issue 4
January 2019
Pages 317-329

Automatic Detection of Salient Video Frames from Wireless Capsule Endoscopy using Adaptive Singular Value Decomposition

References

References

Volume 12, Issue 4January 2019Pages 317-329

Volume 12, Issue 4
January 2019
Pages 317-329