access icon free Integrated system for automatic detection of representative video frames in wireless capsule endoscopy using adaptive sliding window singular value decomposition

© The Institution of Engineering and Technology
Received 05/03/2019, Accepted 02/10/2019, Revised 02/09/2019, Published 08/10/2019

Wireless capsule endoscopy (WCE) is a non-invasive diagnosis method that allows recording a video as the capsule travels through the gastrointestinal (GI) tract. The practical drawback is producing a long clinical video in which the review process by an experienced specialist is tedious. Automated summarisation methods can reduce the evaluation time by experts as well as errors in manual interpretation. The proposed approach consists of three main steps as follows: First, an adaptive sliding window singular value decomposition is employed to extract representative video frames. Then, adaptive contrast diffusion is utilised to increase the visibility of WCE frames. At the end stage, a novel knowledge-based method is developed to segment video frames into four topographic zones of GI tract, which are oesophagus, stomach, small intestine and large intestine. The authors have evaluated the proposed framework in the presence of 30 local datasets as well as publicly available KID database. The average recall and precision were estimated by 0.86 and 0.83, and by 0.82 and 0.83 for KID database, respectively. Their results reveal that significant reduction in the review time is feasible using the proposed technique. Quantitative results of summarisation show that the proposed method is more effective than three methods in the literature.

Inspec keywords: medical image processing; bioacoustics; biomedical optical imaging; singular value decomposition; endoscopes; patient diagnosis; image segmentation; video signal processing; feature extraction

Other keywords: GI tract; capsule travels; integrated system; noninvasive diagnosis method; practical drawback; wireless capsule endoscopy; window singular value decomposition; review time; representative video frames; adaptive contrast diffusion; novel knowledge-based method; automatic detection; segment video frames; WCE frames; automated summarisation methods; evaluation time; long clinical video; gastrointestinal tract; review process

Subjects: Optical, image and video signal processing; Optical and laser radiation (biomedical imaging/measurement); Biology and medical computing; Optical and laser radiation (medical uses); Patient diagnostic methods and instrumentation; Other topics in statistics; Knowledge engineering techniques; Video signal processing; Computer vision and image processing techniques

References

    1. 1)
      • 16. Muhammad, K., Sajjad, M., Young, M., et al: ‘Efficient visual attention driven framework for key frames extraction from hysteroscopy videos’, Biomed. Signal Process. Control, 2017, 33, pp. 161–168.
    2. 2)
      • 1. Fireman, Z., Kopelman, Y.: ‘New frontiers in capsule endoscopy’, J. Gastroenterol. Hepatol., 2007, 22, (8), pp. 1174–1177.
    3. 3)
      • 18. Rapantzikos, K., Kanakis, M.A.: ‘Keyframe extraction from laparoscopic videos based on visual saliency detection’, Comput. Methods Programs Biomed., 2018, 165, pp. 13–23.
    4. 4)
      • 22. Smith, A.R.: ‘Color gamut transform pairs’, ACM Siggraph Comput. Graph., 1978, 12, (3), pp. 12–19.
    5. 5)
      • 25. Perona, P., Malik, J.: ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (7), pp. 629–639.
    6. 6)
      • 27. Wyllie, R., Hyams, J.S.: ‘Pediatric gastrointestinal and liver disease E-book: expert consult-online and print’ (Elsevier Health Sciences, New York, 2010).
    7. 7)
      • 6. Li, B., Meng, M.Q.-H.: ‘Wireless capsule endoscopy images enhancement via adaptive contrast diffusion’, J. Vis. Commun. Image Represent., 2012, 23, (1), pp. 222–228.
    8. 8)
      • 5. Ramaraj, M., Raghavan, S., Khan, W.A.: ‘Homomorphic filtering techniques for WCE image enhancement’. 2013 IEEE Int. Conf. on Computational Intelligence and Computing Research (ICCIC), Enathi, India, 2013, pp. 1–5.
    9. 9)
      • 24. Lillesand, T., Kiefer, R.W., Chipman, J.: ‘Remote sensing and image interpretation’ (John Wiley & Sons, Hoboken, 2014).
    10. 10)
      • 14. Mohammed, A., Yildirim, S., Pedersen, M., et al: ‘Sparse coded handcrafted and deep features for colon capsule video summarization’. IEEE Symp. Comput. Med. Syst., Thessaloniki, Greece, 2017, vol. 17, pp. 728–733.
    11. 11)
      • 3. Mc Caffrey, C., Chevalerias, O., O'Mathuna, C., et al: ‘Swallowable-capsule technology’, IEEE Pervasive Comput.., 2008, 7, (1), pp. 23–29.
    12. 12)
      • 17. Hamza, R., Muhammad, K., Lv, Z., et al: ‘Secure video summarization framework for personalized wireless capsule endoscopy’, Pervasive Mob. Comput., 2017, 41, pp. 436–450.
    13. 13)
      • 19. Al-Sheban, Q., Premaratne, P., McAndrew, D.J., et al: ‘A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy’, Artif. Intell. Med., 2019, 94, pp. 18–27.
    14. 14)
      • 7. Iakovidis, D.K., Koulaouzidis, A.: ‘Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software’, Gastrointest. Endosc., 2014, 80, (5), pp. 877–883.
    15. 15)
      • 13. Chen, J., Wang, Y., Zou, Y.X.: ‘An adaptive redundant image elimination for wireless capsule endoscopy review based on temporal correlation and color-texture feature similarity’. Int. Conf. Digit. Signal Process. DSP, Singapore, 2015, vol. 15, pp. 735–739.
    16. 16)
      • 23. Vadivel, A., Sural, S., Majumdar, A.K.: ‘Human color perception in the HSV space and its application in histogram generation for image retrieval’. Color Imaging: Processing, Hardcopy, and Applications, San Jose, USA, 2005, pp. 598–609.
    17. 17)
      • 20. Golub, G.H., Van Loan, C.F.: ‘Matrix computations’ (JHU Press, Baltimore, 2012).
    18. 18)
      • 9. Mehmood, I., Sajjad, M., Baik, S.W.: ‘Video summarization based tele-endoscopy: a service to efficiently manage visual data generated during wireless capsule endoscopy procedure’, J. Med. Syst., 2014, 38, pp. 109–117.
    19. 19)
      • 4. Gopi, V.P., Palanisamy, P.: ‘Capsule endoscopic image denoising based on double density dual tree complex wavelet transform’, Int. J. Imag. Robot., 2013, 9, pp. 48–60.
    20. 20)
      • 12. Lee, H.G., Choi, M.K., Shin, B.S., et al: ‘Reducing redundancy in wireless capsule endoscopy videos’, Comput. Biol. Med., 2013, 43, (6), pp. 670–682.
    21. 21)
      • 21. Badeau, R., Richard, G., David, B.: ‘Sliding window adaptive SVD algorithms’, IEEE Trans. Signal Process., 2004, 52, (1), pp. 1–10.
    22. 22)
      • 2. Toennies, J.L., Tortora, G., Simi, M., et al: ‘Swallowable medical devices for diagnosis and surgery: the state of the art’, Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 2010, 224, (7), pp. 1397–1414.
    23. 23)
      • 10. Iakovidis, D.K., Tsevas, S., Polydorou, A.: ‘Reduction of capsule endoscopy reading times by unsupervised image mining’, Comput. Med. Imaging Graph., 2010, 34, (6), pp. 471–478.
    24. 24)
      • 29. Davies, D.L., Bouldin, D.W.: ‘A cluster separation measure’, IEEE Trans. Pattern Anal. Mach. Intell., 1979, PAMI-1, (2), pp. 224–227.
    25. 25)
      • 8. Tsevas, S., Iakovidis, D.K., Maroulis, D., et al: ‘Automatic frame reduction of wireless capsule endoscopy video’. 8th IEEE Int. Conf. on BioInformatics and BioEngineering, 2008. BIBE 2008, Athens, Greece, 2008, pp. 1–6.
    26. 26)
      • 11. Zhao, Q., Mullin, G.E., Meng, M.Q.H., et al: ‘A general framework for wireless capsule endoscopy study synopsis’, Comput. Med. Imaging Graph., 2015, 41, pp. 108–116.
    27. 27)
      • 15. Wang, S., Cong, Y., Cao, J., et al: ‘Scalable gastroscopic video summarization via similar-inhibition dictionary selection’, Artif. Intell. Med., 2016, 66, pp. 1–13.
    28. 28)
      • 26. Hasler, D., Suesstrunk, S.E.: ‘Measuring colorfulness in natural images’. Human Vision and Electronic Imaging VIII, Santa Clara, USA, 2003, pp. 87–96.
    29. 29)
      • 28. Koulaouzidis, A., Iakovidis, D.K.: ‘KID: A capsule endoscopy database for medical decision support’. United Eur. Gastroenterol. Week (UEGW), Barcelona, Spain, 2015.
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