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Ulcer Detection in Wireless Capsule Endoscopy Using Locally Computed Features

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Cyber Security and Computer Science (ICONCS 2020)

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Abstract

WCE (Wireless Capsule Endoscopy) has become one of the most significant inventions for detecting different types of digestive tract diseases of humans. Distinct types of abnormalities like polyps, ulcer, tumor and intestine cancer are diagnosed by the clinicians with the implement of WCE in a convenient way. In order to deduce the incubus of the physicians an automated and efficient recognition system is required. In this paper, an advanced method for automatically detecting ulcers in the images of the WCE video record is proposed using the HSV color model. Region of interest (ROI) was identified applying a threshold on images that were extracted from the video of WCE. Local features have been extracted only from the ROI which is usually a small part of an image that offers a low computational cost. Linear discriminant analysis has been used for the separation of ulcer and non-ulcer images. The proposed algorithm was tested on a publicly available database. The performance has obtained accuracy 87.55%, sensitivity 94.70%, specificity 83.30%, precision 75.00% and F1 score 83.70%. Hence, the proposed method outperforms an efficient method that will create a great impact in this research arena.

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References

  1. Death in Different Diseases. http://www.healthdata.org/bangladesh

  2. Kaplan, G.G., Ng, S.C.: Understanding and preventing the global increase of inflammatory bowel disease. Gastroenterology 152, 313–321.e2 (2017). https://doi.org/10.1053/j.gastro.2016.10.020

    Article  Google Scholar 

  3. Kaplan, G.G., Jess, T.: The changing landscape of inflammatory bowel disease: East meets West. Gastroenterology 150, 24–26 (2016). https://doi.org/10.1053/j.gastro.2015.11.029

    Article  Google Scholar 

  4. Ghosh, T., Das, A., Sayed, R.: Automatic small intestinal ulcer detection in capsule endoscopy images. Int. J. Sci. Eng. Res. 7, 737–741 (2016)

    Google Scholar 

  5. Francis, R.D.: Sensitivity and specificity of the red blood identification (RBIS) in video capsule endoscopy. In: 3rd International Conference on Capsule Endoscopy 2004 (2004)

    Google Scholar 

  6. Coimbra, M.T., Cunha, J.P.S.: MPEG-7 visual descriptors—contributions for automated feature extraction in capsule endoscopy. IEEE Trans. Circuits Syst. Video Technol. 16, 628–637 (2006). https://doi.org/10.1109/TCSVT.2006.873158

    Article  Google Scholar 

  7. Li, B., Meng, M.Q.-H.: Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Comput. Biol. Med. 39, 141–147 (2009). https://doi.org/10.1016/j.compbiomed.2008.11.007

    Article  Google Scholar 

  8. Ghosh, T., Bashar, S.K., Alam, Md.S., Wahid, K., Fattah, S.A.: A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images. In: 2014 International Conference on Informatics, Electronics & Vision (ICIEV), pp. 1–4. IEEE (2014)

    Google Scholar 

  9. Ghosh, T., Fattah, S.A., Shahnaz, C., Wahid, K.A.: An automatic bleeding detection scheme in wireless capsule endoscopy based on histogram of an RGB-indexed image. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 4683–4686. IEEE (2014)

    Google Scholar 

  10. Li, B., Meng, M.Q.H.: Texture analysis for ulcer detection in capsule endoscopy images. Image Vis. Comput. 27, 1336–1342 (2009). https://doi.org/10.1016/j.imavis.2008.12.003

    Article  Google Scholar 

  11. Szczypiński, P., Klepaczko, A., Pazurek, M., Daniel, P.: Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput. Methods Programs Biomed. 113, 396–411 (2014). https://doi.org/10.1016/j.cmpb.2012.09.004

    Article  Google Scholar 

  12. Yeh, J.-Y., Wu, T.-H., Tsai, W.-J.: Bleeding and ulcer detection using wireless capsule endoscopy images. J. Softw. Eng. Appl. 07, 422–432 (2014). https://doi.org/10.4236/jsea.2014.75039

    Article  Google Scholar 

  13. Hossain, M.S., Al Mamun, A., Hasan, M.G., Hossain, M.M.: Easy scheme for ulcer detection in wireless capsule endoscopy images. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–5. IEEE (2019)

    Google Scholar 

  14. Al Mamun, A., Hossain, M.S.: Ulcer detection in image converted from video footage of wireless capsule endoscopy. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–4. IEEE (2019)

    Google Scholar 

  15. Al Mamun, A., Hossain, M.S., Hossain, M.M., Hasan, M.G.: Discretion way for bleeding detection in wireless capsule endoscopy images. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–6. IEEE (2019)

    Google Scholar 

  16. Kundu, A.K., Fattah, S.A.: An asymmetric indexed image based technique for automatic ulcer detection in wireless capsule endoscopy images. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 734–737. IEEE (2017)

    Google Scholar 

  17. The capsule endoscopy website. https://www.capsuleendoscopy.org

  18. Protiere, A., Sapiro, G.: Interactive image segmentation via adaptive weighted distances. IEEE Trans. Image Process. 16, 1046–1057 (2007). https://doi.org/10.1109/TIP.2007.891796

    Article  MathSciNet  Google Scholar 

  19. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-662-05088-0

    Book  MATH  Google Scholar 

  20. Lam, L., Lee, S.-W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14, 869–885 (1992). https://doi.org/10.1109/34.161346

    Article  Google Scholar 

  21. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  22. Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools. 12, 13–21 (2007). https://doi.org/10.1080/2151237X.2007.10129236

    Article  Google Scholar 

  23. Meyer, F.: Topographic distance and watershed lines. Signal Process. 38, 113–125 (1994). https://doi.org/10.1016/0165-1684(94)90060-4

    Article  MATH  Google Scholar 

  24. Gonzales, R., Woods, R., Eddine, S.: Digital Image Processing using MATLAB 2nd edn. (2009)

    Google Scholar 

  25. Kaur, S., Banga, D.V.K.: Content Based Image Retrieval: Survey and Comparison between RGB and HSV Model (2013)

    Google Scholar 

  26. Fattah, S.A., et al.: An approach for formant based speech recognition in noise. In: TENCON 2012 IEEE Region 10 Conference, pp. 1–4. IEEE (2012)

    Google Scholar 

  27. Altman, D.G., Bland, J.M.: Statistics notes: diagnostic tests 1: sensitivity and specificity. BMJ 308, 1552 (1994). https://doi.org/10.1136/bmj.308.6943.1552

    Article  Google Scholar 

  28. Sasaki, Y.: The Truth of the F-measure. Teach Tutor Mater (2007)

    Google Scholar 

  29. Karargyris, A., Bourbakis, N.: Identification of ulcers in wireless capsule endoscopy videos. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 554–557. IEEE (2009)

    Google Scholar 

  30. Eid, A., Charisis, V.S., Hadjileontiadis, L.J., Sergiadis, G.D.: A curvelet-based lacunarity approach for ulcer detection from Wireless Capsule Endoscopy images. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 273–278. IEEE (2013)

    Google Scholar 

  31. Kundu, A.K., Bhattacharjee, A., Fattah, S.A., Shahnaz, C.: An Automatic Ulcer Detection Scheme Using Histogram in YIQ Domain from Wireless Capsule Endoscopy Images (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna, 6600, Bangladesh

    Md. Sohag Hossain, Md. Galib Hasan & Md. Motaher Hossain

  2. Faculty of Engineering and Technology, Multimedia University, 75450, Melaka, Malaysia

    Abdullah Al Mamun

  3. Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, 35487, USA

    Tonmoy Ghosh

  4. School of Engineering + Technology, Western Carolina University, Cullowhee, NC, 28723, USA

    Anik Tahabilder

Authors
  1. Md. Sohag Hossain
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  2. Abdullah Al Mamun
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  3. Tonmoy Ghosh
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  4. Md. Galib Hasan
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  5. Md. Motaher Hossain
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  6. Anik Tahabilder
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Corresponding author

Correspondence to Abdullah Al Mamun .

Editor information

Editors and Affiliations

  1. Daffodil International University, Dhaka, Bangladesh

    Touhid Bhuiyan

  2. Daffodil International University, Dhaka, Bangladesh

    Md. Mostafijur Rahman

  3. Daffodil International University, Dhaka, Bangladesh

    Md. Asraf Ali

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Hossain, M.S., Al Mamun, A., Ghosh, T., Hasan, M.G., Hossain, M.M., Tahabilder, A. (2020). Ulcer Detection in Wireless Capsule Endoscopy Using Locally Computed Features. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_39

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  • DOI: https://doi.org/10.1007/978-3-030-52856-0_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-52855-3

  • Online ISBN: 978-3-030-52856-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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