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Multimedia Big Data Computing for IoT Applications pp 269–285Cite as

Data Reduction Technique for Capsule Endoscopy

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 163))

Abstract

The advancements in the field of IoT and sensors generate a huge amount of data. This huge data serves as an input to knowledge discovery and machine learning producing unprecedented results leading to trend analysis, classification, prediction, fraud and fault detection, drug discovery, artificial intelligence and many more. One such cutting-edge technology is capsule endoscopy (CE). CE is a noninvasive, non-sedative, patient-friendly and particularly child-friendly alternative to conventional endoscopy for diagnosis of gastrointestinal tract diseases. However, CE generates approximately 60000 images from each video. Further, when computer vision and pattern recognition techniques are applied to CE images for disease detection, the resultant data called feature vector sizes to 181548 for one image. Now a machine learning task for computer-aided disease detection would include nothing less than thousands of images leading to highly data intensive task. Processing such huge amount of data is an expensive task in terms of computation, memory and time. Hence, a data reduction technique needs to be employed in such a way that minimum information is lost. It is important to note that features must be discriminative and thus redundant or correlative data is not very useful. In this study, a data reduction technique is designed with the aim of maximizing the information gain. This technique exhibits high variance and low correlation to achieve this task. The data reduced feature vector is fed to a computer based diagnosis system in order to detect ulcer in the gastrointestinal tract. The proposed data reduction technique reduces the feature set to 98.34%.

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References

  1. A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. Maasberg, K.-K.R. Choo, Multimedia big data computing and Internet of Things applications: a taxonomy and process model. J. Netw. Comput. Appl. [Internet] 124, 169–195 (2018), https://linkinghub.elsevier.com/retrieve/pii/S1084804518303011

    Article  Google Scholar 

  2. S. Charfi, Ansari M. El, Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images. Multimed. Tools Appl. 77(3), 4047–4064 (2018)

    Article  Google Scholar 

  3. S. Suman, F.A. Hussin, A.S. Malik, S.H. Ho, I. Hilmi, A.H.-R. Leow, et al., Feature selection and classification of ulcerated lesions using statistical analysis for WCE images. Appl. Sci. 7(10) (2017)

    Article  Google Scholar 

  4. S. Tanwar, P. Patel, K. Patel, S. Tyagi, N. Kumar, M.S. Obaidat, An advanced Internet of Thing based security alert system for smart home, in IEEE CITS 2017: 2017 International Conference on Computer, Information and Telecommunication Systems (2017), pp. 25–29

    Google Scholar 

  5. Capsule image 1 [Internet]. [cited 2018 Mar 6], https://commons.wikimedia.org/w/index.php?curid=819896

  6. Capsule image 2 [Internet]. [cited 2018 Mar 6], https://www.ecnmag.com/article/2012/02/reducing-size-while-improving-functionality-and-safety-next-generation-medical-device-design

  7. G. Liu, G. Yan, S. Kuang, Y. Wang, Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy. Comput. Biol. Med. [Internet] 70, 131–138 (2016). http://dx.doi.org/10.1016/j.compbiomed.2016.01.021

    Article  Google Scholar 

  8. Q. Zhao, G.E. Mullin, M.Q.H. Meng, T. Dassopoulos, R. Kumar, A general framework for wireless capsule endoscopy study synopsis. Comput. Med. Imaging Graph [Internet] 41, 108–116 (2015). http://dx.doi.org/10.1016/j.compmedimag.2014.05.011

    Article  Google Scholar 

  9. A. Srivastava, S.K. Singh, S. Tanwar, S. Tyagi, Suitability of big data analytics in Indian banking sector to increase revenue and profitability, in Proceedings of 2017, 3rd International Conference on Advances in Computing Communication & Automation (Fall), ICACCA 2017, 1–4 January 2018 (2018), pp. 1–4

    Google Scholar 

  10. A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, Fog computing for Healthcare 4.0 environment: opportunities and challenges. Comput. Electr. Eng. [Internet] 72, 1–13 (2018). https://doi.org/10.1016/j.compeleceng.2018.08.015

    Article  Google Scholar 

  11. N.I.R. Yassin, S. Omran, E.M.F. El Houby, H. Allam, Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput. Methods Programs Biomed. [Internet] 156, 25–45 (2017), http://linkinghub.elsevier.com/retrieve/pii/S0169260717306405

    Article  Google Scholar 

  12. R. Srivastava, S. Srivastava, Restoration of Poisson noise corrupted digital images with nonlinear PDE based filters along with the choice of regularization parameter estimation. Pattern Recognit. Lett. [Internet] 34(10), 1175–1185 (2013). http://dx.doi.org/10.1016/j.patrec.2013.03.026

    Article  Google Scholar 

  13. V.S. Kodogiannis, M. Boulougoura, J.N. Lygouras, I. Petrounias, A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images. Neurocomputing 70(4–6), 704–717 (2007)

    Article  Google Scholar 

  14. B. Li, M.Q.H. Meng, Wireless capsule endoscopy images enhancement via adaptive contrast diffusion. J. Vis. Commun. Image Represent [Internet] 23(1), 222–228 (2012). http://dx.doi.org/10.1016/j.jvcir.2011.10.002

    Article  Google Scholar 

  15. M. Mackiewicz, J. Berens, M. Fisher, Wireless capsule endoscopy color video segmentation. IEEE Trans. Med. Imaging 27(12), 1769–1781 (2008)

    Article  Google Scholar 

  16. Y. Lan, X. Zhang, Z. Liu, L. Zhao, M. Li, Hybrid segmentation using region information for wireless capsule endoscopy images. Inf. Technol. J. 12(16), 3815–3819 (2013)

    Article  Google Scholar 

  17. Y. Shen, P.P. Guturu, B.P. Buckles, Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features. IEEE Trans. Inf. Technol. Biomed. [Internet] 16(1), 98–105 (2012), http://www.ncbi.nlm.nih.gov/pubmed/22010158

  18. X. Jiang, Feature extraction for image recognition and computer vision, in Proceedings of 2009, 2nd IEEE International Conference on Computer Science and Information Technology ICCSIT 2009 (2009), pp. 1–15

    Google Scholar 

  19. A. Karargyris, N. Bourbakis, Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans. Biomed. Eng. 58(10 PART 1), 2777–2786 (2011)

    Article  Google Scholar 

  20. Y. Yuan, J. Wang, B. Li, M.Q.H. Meng, Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans. Med. Imaging. 34(10), 2046–2057 (2015)

    Article  Google Scholar 

  21. B. Li, M.Q.H. Meng, Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Comput. Biol. Med. 39(2), 141–147 (2009)

    Article  Google Scholar 

  22. B. Li, M.Q.H. Meng, Texture analysis for ulcer detection in capsule endoscopy images. Image Vis. Comput. [Internet] 27(9), 1336–1342 (2009). http://dx.doi.org/10.1016/j.imavis.2008.12.003

    Article  Google Scholar 

  23. V.S. Charisis, L.J. Hadjileontiadis, C.N. Liatsos, C.C. Mavrogiannis, G.D. Sergiadis, Capsule endoscopy image analysis using texture information from various colour models. Comput. Methods Programs Biomed. [Internet] 107(1), 61–74 (2012). http://dx.doi.org/10.1016/j.cmpb.2011.10.004

    Article  Google Scholar 

  24. P. Szczypiński, A. Klepaczko, M. Pazurek, P. Daniel, Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput. Methods Programs Biomed. 113(1), 396–411 (2014)

    Article  Google Scholar 

  25. R. Nawarathna, J. Oh, J. Muthukudage, W. Tavanapong, J. Wong, P.C. de Groen, et al., Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing [Internet] 144, 70–91 (2014), http://linkinghub.elsevier.com/retrieve/pii/S0925231214007334

    Article  Google Scholar 

  26. S.I. Sahidan, M.Y. Mashor, A.S.W. Wahab, Z. Salleh, H. Ja’afar, Local and global contrast stretching for color contrast enhancement on Zehl-Nelsen tissue section slide images. in IFMBE Proc. 2008, vol. 21, no. 1 (IFMBE, 2008), pp. 583–586

    Google Scholar 

  27. M. Moradi, A. Falahati, A. Shahbahrami, R. Zare-Hassanpour, Improving visual quality in wireless capsule endoscopy images with contrast-limited adaptive histogram equalization, in 2015 2nd International Conference Pattern Recognition and Image Analysis IPRIA 2015 (2015), pp. 0–4

    Google Scholar 

  28. Y.J. Cho, S.H. Bae, K.J. Yoon, Multi-classifier-based automatic polyp detection in endoscopic images. J. Med. Biol. Eng. 36(6), 871–882 (2016)

    Article  Google Scholar 

  29. N. Dalal, W. Triggs, Histograms of oriented gradients for human detection, in 2005 Conference on Computer Vision & Pattern Recognition CVPR 2005 [Internet], vol. 1, no. 3 (IEEE Computer Society, 2004), pp. 886–893, http://eprints.pascal-network.org/archive/00000802/

  30. T. Ojala, M. Pietikäinen, T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  31. A. Jain, D. Zongker, Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Trans. Pattern Anal. Mach. Intell. [Internet] 19(2), 153–158 (1997), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=574797

  32. D. Tao, X. Tang, X. Wu, Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1088–1099 (2006)

    Article  Google Scholar 

  33. E. Spyrou, D.K. Iakovidis, Video-based measurements for wireless capsule endoscope tracking. Meas. Sci. Technol. 25(1) (2014)

    Article  Google Scholar 

  34. V.P. Singh, R. Srivastava, Improved image retrieval using fast Colour-texture features with varying weighted similarity measure and random forests. Multimed. Tools Appl. 1–26 (2017)

    Google Scholar 

  35. I. Kononenko, E. Šimec, M. Robnik-Šikonja, Overcoming the myopic of inductive learning algorithms with RELIEFF. Appl. Intell. [Internet] 7(1), 39–55 (1997), http://citeseer.nj.nec.com/kononenko97overcoming.html

  36. Q. Gu, Z. Li, J. Han, Generalized Fisher Score For Feature Selection (2005)

    Google Scholar 

  37. B. Liao, Y. Jiang, W. Liang, W. Zhu, L. Cai, Z. Cao, Gene selection using locality sensitive Laplacian score. IEEE/ACM Trans. Comput. Biol. Bioinforma. 11(6), 1146–1156 (2014)

    Article  Google Scholar 

  38. N.E. Koshy, V.P. Gopi, A new method for ulcer detection in endoscopic images, in 2nd International Conference on Electronics and Communication Systems ICECS 2015 (2015), pp. 1725–1729

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Indian Institute of Technology (BHU), Varanasi, India

    Kuntesh Jani & Rajeev Srivastava

Authors
  1. Kuntesh Jani
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  2. Rajeev Srivastava
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Correspondence to Kuntesh Jani .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  2. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala, Punjab, India

    Sudhanshu Tyagi

  3. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala, Punjab, India

    Neeraj Kumar

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Jani, K., Srivastava, R. (2020). Data Reduction Technique for Capsule Endoscopy. In: Tanwar, S., Tyagi, S., Kumar, N. (eds) Multimedia Big Data Computing for IoT Applications. Intelligent Systems Reference Library, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-13-8759-3_10

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