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A locally based feature descriptor for abnormalities detection

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Abstract

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Therefore, it has been gradually adopted compared with traditional endoscopies for gastrointestinal diseases. However, the task of reviewing the vast amount of images produced by a WCE test is exhaustive for the physicians. This paper presents a new feature extraction scheme for pathological inflammation and ulcer regions discrimination in WCE images. In addition, the novel approach is adopted for polyp recognition in colonoscopy videos. A novel idea based on extracting certain local features from the image is proposed. Then, the occurrence histogram of these features is used as descriptor of the image. The new feature descriptor scheme is grayscale rotation invariant and computationally simple as the operator can be realized with a few operations in a small neighborhood. The proposed operator does not discard the contrast information. Besides, we propose to test the quality of the model using logarithmic loss metric and show how calibration can be useful in reducing the aforementioned measure. Extensive classification experiments have been applied on different datasets, which prove that the occurrence histogram of the extracted features is powerful. The proposed method achieved 99.1%, 99.7% and 99.2% in terms of the precision in the first, second and third datasets, respectively, and surpassed some known local descriptors on a texture dataset.

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References

  1. Adler DG, Gostout CJ (2003) Wireless capsule endoscopy. Hosp Phys 405(5):14–22

  2. Ameling S, Wirth S, Paulus D, Lacey G, Vilario F (2009) Texture-based polyp detection in colonoscopy. pp 346–350

  3. Barbosa DJC, Ramos J, Correia JH, Lima CS (2009) Automatic detection of small bowel tumors in capsule endoscopy based on color curvelet covariance statistical texture descriptors. In: Conference and proceedings of IEEE engineering in medicine and biology society. pp 6683–6686

  4. Charfi S, El Ansari M (2017a) Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images. Multimed Tools Appl 77:1–18

  5. Charfi S, El Ansari M (2017b) Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy videos. In: International conference on advanced technologies for signal and image processing (ATSIP), IEEE, pp 1–5

  6. Charisis VS, Katsimerou C, Hadjileontiadis LJ, Liatsos CN, Sergiadis GD (2013) Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: an educational tool to physicians. In: 2013 IEEE 26th international symposium on computer-based medical systems (CBMS), IEEE, pp 203–208

  7. Charisis VS, Hadjileontiadis LJ, Liatsos CN, Mavrogiannis CC, Sergiadis GD (2012) Capsule endoscopy image analysis using texture information from various colour models. Comput Meth Prog Bio 107(1):61–74

  8. Cimpoi M, Maji S, Kokkinos I, Mohamed S, Vedaldi A (2014) Describing textures in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

  9. Cimpoi M, Maji S, Kokkinos I, Mohamed S, Vedaldi A (2018) Describable textures dataset

  10. Committee AT, Wang A, Banerjee S, Barth BA, Bhat YM, Chauhan S, Gottlieb KT, Konda V, Maple JT, Murad F, Pfau PR, Pleskow DK, Siddiqui UD, Tokar JL, Rodriguez SA (2013) Wireless capsule endoscopy. Gastrointest Endosc 78:805–815

  11. El Ansari M, Charfi S (2017) Computer-aided system for polyp detection in wireless capsule endoscopy images. In: 2017 International conference on wireless networks and mobile communications (WINCOM), IEEE, pp 1–6

  12. Endoscopy C (2018) Capsule endoscopy products. www.capsuleendoscopy.org

  13. Fan S, Xu L, Fan Y, Wei K, Li L (2018) Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol 63(16):165001

  14. Ghosh T, Das A, Sayed R (2016) Automatic small intestinal ulcer detection in capsule endoscopy images. Int J Sci Eng Res 7(10):737–741

  15. Girgis HZ, Mitchell BR, Dassopouios T, Mullin G, Haga G (2010) An intelligent system to detect crohn’s disease inflammation in wireless capsule endoscopy videos. In: ISBI, IEEE, pp 1373–1376

  16. Häfner M, Tamaki T, Tanaka S, Uhl A, Wimmer G, Yoshida S (2015) Local fractal dimension based approaches for colonic polyp classification. Med Image Anal 26(1):92–107

  17. Iakovidis DK, Koulaouzidis A (2014) Automatic lesion detection in wireless capsule endoscopy: a simple solution for a complex problem. In: 2014 IEEE International conference on image processing, ICIP 2014, Paris, France, pp 2236–2240

  18. Iddan G, Meron G, Glukhovsky A, Swain P (2000) Wireless capsule endoscopy. Nature 405(6785):405–417

  19. Jia X, Meng MQ (2016) A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. In: 38th Annual international conference of the IEEE engineering in medicine and biology society, EMBC 2016, Orlando, FL, USA, pp 639–642

  20. Kodogiannis VS, Boulougoura M, Lygouras JN, Petrounias I (2007a) A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images. Neurocomput 70(4–6):704–717

  21. Kodogiannis VS, Boulougoura M, Wadge E, Lygouras JN (2007b) The usage of soft-computing methodologies in interpreting capsule endoscopy. Eng Appl Artif Intell 20(4):539–553

  22. Kopylov U, Carter D, Eliakim AR (2016) Capsule endoscopy and deep enteroscopy in irritable bowel disease. Gastrointest Endosc Clin N Am 26(4):611–627

  23. Koshy NE, Gopi VP (2015) A new method for ulcer detection in endoscopic images. In: 2015 2nd International conference on electronics and communication systems (ICECS), IEEE, pp 1725–1729

  24. Kundu A, Bhattacharjee A, Fattah S, Shahnaz C (2017) An automatic ulcer detection scheme using histogram in YIQ domain from wireless capsule endoscopy images. In: Region 10 Conference, TENCON 2017-2017 IEEE, IEEE, pp 1300–1303

  25. Kundu A, Fattah S (2017) 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), IEEE, pp 734–737

  26. Li B, Meng MQH (2009b) Texture analysis for ulcer detection in capsule endoscopy images. Image Vis Comput 27(9):1336–1342

  27. Li B, Meng MQH (2012) Automatic polyp detection for wireless capsule endoscopy images. Expert Syst Appl 39(12):10952–10958

  28. Li B, Meng MQH, Lau JYW (2011) Computer-aided small bowel tumor detection for capsule endoscopy. Artif Intell Med 52(1):11–16

  29. Li B, Xu G, Zhou R, Wang T (2015) Computer aided wireless capsule endoscopy video segmentation. Med Phys 42:645–652

  30. Li B, Meng MQH (2009a) Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine. In: Proceedings of the 2009 IEEE/RSJ international conference on intelligent robots and systems IROS’09, IEEE Press, Piscataway, pp 498–503

  31. Liu P, Choo KKR, Wang L, Huang F (2017) Svm or deep learning? A comparative study on remote sensing image classification. Soft Comput 21(23):7053–7065

  32. Maghsoudi OH, Soltanian-Zadeh H (2013) Detection of abnormalities in wireless capsule endoscopy frames using local fuzzy patterns. In: 2013 IEEE 20th Iranian conference on biomedical engineering, ICBME 2013, Tehran, pp 286–291

  33. Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053

  34. Mitselos IV, Christodoulou DK, Katsanos KH, Tsianos EV (2015) Role of wireless capsule endoscopy in the follow-up of inflammatory bowel disease. World J Gastrointest Endosc 7:643–651

  35. Ogiela MR, Krzyworzeka N (2016) Heuristic approach for computer-aided lesion detection in mammograms. Soft Comput 20(10):4193–4202

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

  37. Platt J et al (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10(3):61–74

  38. Ponte A, Pinho R, Rodrigues A, Silva J, Rodrigues J, Sousa M, Carvalho J (2017) Evaluation and comparison of capsule endoscopy scores for assessment of inflammatory activity of small-bowel in crohn’s disease. Gastroenterologia y hepatologia 41:245–250

  39. Riegler M, Pogorelov K, Markussen J, Lux M, Stensland HK, de Lange T, Griwodz C, Halvorsen P, Johansen D, Schmidt PT, Eskeland SL (2016) Computer aided disease detection system for gastrointestinal examinations. In: Proceedings of the 7th international conference on multimedia systems MMSys ’16, ACM, , pp 29:1–29:4. https://doi.org/10.1145/2910017.2910629

  40. Rokkas T, Papaxoinis K, Triantafyllou K, Ladas SD (2010) A meta-analysis evaluating the accuracy of colon capsule endoscopy in detecting colon polyps. Gastrointest Endosc 71(4):792–798

  41. Saurin JC, Beneche N, Chambon C, Pioche M (2016) Challenges and future of wireless capsule endoscopy. Clin Endosc 42:26–29

  42. Seguí S, Drozdzal M, Pascual G, Radeva P, Malagelada C, Azpiroz F, Vitrià J (2016) Generic feature learning for wireless capsule endoscopy analysis. Comput Biol Med 79:163–172

  43. Ševo I, Avramović A, Balasingham I, Elle OJ, Bergsland J, Aabakken L (2016) Edge density based automatic detection of inflammation in colonoscopy videos. Comput Biol Med 72:138–150

  44. Shin Y, Balasingham I (2018) Automatic polyp frame screening using patch based combined feature and dictionary learning. Comput Med Imaging Graph 69:33–42

  45. Souaidi M, Abdelouahed AA, El Ansari M (2018) Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images. Multimed Tools Appl 78:1–18

  46. Suman S, Hussin FA, Nicolas W, Malik AS (2016) Ulcer detection and classification of wireless capsule endoscopy images using rgb masking. Adv Sci Lett 22(10):2764–2768

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

  48. Tajbakhsh N, Gurudu SR, Liang J (2016) Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging 35(2):630–644

  49. Walsh CG, Sharman K, Hripcsak G (2017) Beyond discrimination: a comparison of calibration methods and clinical usefulness of predictive models of readmission risk. J Biomed Inform 76:9–18

  50. Wimmer G, Tamaki T, Tischendorf JJW, Häfner M, Yoshida S, Tanaka S, Uhl A (2016) Directional wavelet based features for colonic polyp classification. Med Image Anal 31:16–36

  51. Yeh JY, Wu TH, Tsai WJ (2014) Bleeding and ulcer detection using wireless capsule endoscopy images. J Softw Eng Appl 7(05):422

  52. Yu L, Chen H, Dou Q, Qin J, Heng PA (2017) Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inform 21(1):65–75

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

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Acknowledgements

We gratefully acknowledge and express our thanks to the National Center for Scientific and technical Research (CNRST) in Rabat for its research grant.

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Correspondence to Said Charfi.

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Charfi, S., El Ansari, M. A locally based feature descriptor for abnormalities detection. Soft Comput 24, 4469–4481 (2020). https://doi.org/10.1007/s00500-019-04208-8

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Keywords

  • Ulcer
  • Inflammatory
  • Polyp
  • Feature extraction
  • Contrast