http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

References

    1. 1)
      • 1. Fireman, Z., Kopelman, Y.: ‘New frontiers in capsule endoscopy’, J. Gastroenterol. Hepatol., 2007, 22, (8), pp. 11741177.
    2. 2)
      • 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. 13971414.
    3. 3)
      • 3. Mc Caffrey, C., Chevalerias, O., O'Mathuna, C., et al: ‘Swallowable-capsule technology’, IEEE Pervasive Comput.., 2008, 7, (1), pp. 2329.
    4. 4)
      • 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. 4860.
    5. 5)
      • 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. 15.
    6. 6)
      • 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. 222228.
    7. 7)
      • 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. 877883.
    8. 8)
      • 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. 16.
    9. 9)
      • 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. 109117.
    10. 10)
      • 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. 471478.
    11. 11)
      • 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. 108116.
    12. 12)
      • 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. 670682.
    13. 13)
      • 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. 735739.
    14. 14)
      • 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. 728733.
    15. 15)
      • 15. Wang, S., Cong, Y., Cao, J., et al: ‘Scalable gastroscopic video summarization via similar-inhibition dictionary selection’, Artif. Intell. Med., 2016, 66, pp. 113.
    16. 16)
      • 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. 161168.
    17. 17)
      • 17. Hamza, R., Muhammad, K., Lv, Z., et al: ‘Secure video summarization framework for personalized wireless capsule endoscopy’, Pervasive Mob. Comput., 2017, 41, pp. 436450.
    18. 18)
      • 18. Rapantzikos, K., Kanakis, M.A.: ‘Keyframe extraction from laparoscopic videos based on visual saliency detection’, Comput. Methods Programs Biomed., 2018, 165, pp. 1323.
    19. 19)
      • 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. 1827.
    20. 20)
      • 20. Golub, G.H., Van Loan, C.F.: ‘Matrix computations’ (JHU Press, Baltimore, 2012).
    21. 21)
      • 21. Badeau, R., Richard, G., David, B.: ‘Sliding window adaptive SVD algorithms’, IEEE Trans. Signal Process., 2004, 52, (1), pp. 110.
    22. 22)
      • 22. Smith, A.R.: ‘Color gamut transform pairs’, ACM Siggraph Comput. Graph., 1978, 12, (3), pp. 1219.
    23. 23)
      • 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. 598609.
    24. 24)
      • 24. Lillesand, T., Kiefer, R.W., Chipman, J.: ‘Remote sensing and image interpretation’ (John Wiley & Sons, Hoboken, 2014).
    25. 25)
      • 25. Perona, P., Malik, J.: ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (7), pp. 629639.
    26. 26)
      • 26. Hasler, D., Suesstrunk, S.E.: ‘Measuring colorfulness in natural images’. Human Vision and Electronic Imaging VIII, Santa Clara, USA, 2003, pp. 8796.
    27. 27)
      • 27. Wyllie, R., Hyams, J.S.: ‘Pediatric gastrointestinal and liver disease E-book: expert consult-online and print’ (Elsevier Health Sciences, New York, 2010).
    28. 28)
      • 28. Koulaouzidis, A., Iakovidis, D.K.: ‘KID: A capsule endoscopy database for medical decision support’. United Eur. Gastroenterol. Week (UEGW), Barcelona, Spain, 2015.
    29. 29)
      • 29. Davies, D.L., Bouldin, D.W.: ‘A cluster separation measure’, IEEE Trans. Pattern Anal. Mach. Intell., 1979, PAMI-1, (2), pp. 224227.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2019.0251
Loading

Related content

content/journals/10.1049/iet-ipr.2019.0251
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address