Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy images

© The Institution of Engineering and Technology
Received 20/09/2018, Accepted 11/02/2019, Revised 12/01/2019, Published 22/02/2019

Wireless capsule endoscopy (WCE) has revolutionised the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large number of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reduce the diagnosis time and improve the detection accuracy. To address this problem, the authors propose a CAD system for automatic detection of ulcer in WCE images. Firstly, they enhance the input images to be better exploited in the main steps of the proposed method. Afterward, segmentation using saliency map-based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden Markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.

Inspec keywords: image classification; image segmentation; image texture; medical image processing; support vector machines; biomedical optical imaging; endoscopes; hidden Markov models; diseases; feature extraction; multilayer perceptrons

Other keywords: segmented regions; diagnosis time; gastrointestinal tract; ulcerous regions; traditional endoscopies; computer-aided diagnosis system; wireless capsule endoscopy; ulcer detection; saliency map-based texture; CAD system; detection accuracy; colour; WCE images; input images

Subjects: Optical and laser radiation (medical uses); Optical and laser radiation (biomedical imaging/measurement); Other topics in statistics; Neural computing techniques; Fluctuation phenomena, random processes, and Brownian motion; Biology and medical computing; Markov processes; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation; Knowledge engineering techniques; Optical, image and video signal processing

References

    1. 1)
      • 42. Kopylov, U., Carter, D., Eliakim, A.R.: ‘Capsule endoscopy and deep enteroscopy in irritable bowel disease’, Gastrointest. Endosc. Clin. N. Am., 2016, 26, (4), pp. 611–627.
    2. 2)
      • 7. Ren, Y.-F., Mu, Z.-C.: ‘Salient object detection based on global contrast on texture and color’. 2014 Int. Conf. on Machine Learning and Cybernetics (ICMLC), 2014, vol. 1, pp. 7–12.
    3. 3)
      • 51. Pohle, J., Langrock, R., van Beest, F.M., et al: ‘Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement’, J. Agric., Biol. Environ. Stat., 2017, 22, (3), pp. 270–293.
    4. 4)
      • 8. Kundu, A., Bhattacharjee, A., Fattah, S., et al: ‘Automatic ulcer detection scheme using gray scale histogram from wireless capsule endoscopy’. 2016 IEEE Int. WIE Conf. on Electrical and Computer Engineering (WIECON-ECE), 2016, pp. 242–245.
    5. 5)
      • 3. Qureshi, W.A.: ‘Current and future applications of the capsule camera’, Nat. Rev. Drug Discovery, 2004, 3, (5), pp. 447–450.
    6. 6)
      • 57. Barkan, O., Weill, J., Wolf, L., et al: ‘Fast high dimensional vector multiplication face recognition’. Proc. of the IEEE Int. Conf. on Computer Vision, 2013, pp. 1960–1967.
    7. 7)
      • 44. Mitselos, I.V., Christodoulou, D.K., Katsanos, K.H., et al: ‘Role of wireless capsule endoscopy in the follow-up of inflammatory bowel disease’, World J. Gastrointest. Endosc., 2015, 7, pp. 643–651.
    8. 8)
      • 19. Szczypínski, P., Klepaczko, A., Pazurek, M., et al: ‘Texture and color based image segmentation and pathology detection in capsule endoscopy videos’, Comput. Methods Programs Biomed., 2014, 113, (1), pp. 396–411.
    9. 9)
      • 18. Charisis, V.S., Katsimerou, C., Hadjileontiadis, L.J., et al: ‘Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: an educational tool to physicians’. Proc. IEEE Symp. Computer-Based Medical Systems, 2013, pp. 203–208.
    10. 10)
      • 26. Souaidi, M., Charfi, S., Abdelouahad, A.A., et al: ‘New features for wireless capsule endoscopy polyp detection’. 2018 Int. Conf. on Intelligent Systems and Computer Vision (ISCV), April 2018, pp. 1–6.
    11. 11)
      • 12. Li, B., Meng, M.Q.H.: ‘Texture analysis for ulcer detection in capsule endoscopy images’, Image Vision Comput., 2009, 27, (9), pp. 1336–1342.
    12. 12)
      • 16. Yuan, Y., Wang, J., Li, B., et al: ‘Saliency based ulcer detection for wireless capsule endoscopy diagnosis’, IEEE Trans. Med. Imag., 2015, 34, (10), pp. 2046–2057.
    13. 13)
      • 43. Rokkas, T., Papaxoinis, K., Triantafyllou, K., et al: ‘A meta-analysis evaluating the accuracy of colon capsule endoscopy in detecting colon polyps’, Gastrointest. Endosc., 2010, 71, (4), pp. 792–798.
    14. 14)
      • 9. Kundu, A., Bhattacharjee, A., Fattah, S., et al: ‘An automatic ulcer detection scheme using histogram in YIQ domain from wireless capsule endoscopy images’. TENCON 2017-2017 IEEE Region 10 Conf., 2017, pp. 1300–1303.
    15. 15)
      • 54. C. Endoscopy: ‘Capsule endoscopy products’, 2018, pp. 1–6.
    16. 16)
      • 31. Usman, M.A., Satrya, G.B., Usman, M.R., et al: ‘Detection of small colon bleeding in wireless capsule endoscopy videos’, Comput. Med. Imaging Graph., 2016, 54, pp. 16–26.
    17. 17)
      • 35. Wimmer, G., Tamaki, T., Tischendorf, J.J.W., et al: ‘Directional wavelet based features for colonic polyp classification’, Med. Image Anal., 2016, 31, pp. 16–36.
    18. 18)
      • 58. Banerji, S., Verma, A., Liu, C.: ‘Novel color LBP descriptors for scene and image texture classification’. 15th Int. Conf. on Image Processing, Computer Vision, Pattern Recognition, Las Vegas, Nevada, 2011, pp. 537–543.
    19. 19)
      • 17. Charfi, S., El Ansari, M.: ‘Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy videos’. 2017 Int. Conf. on Advanced Technologies for Signal and Image Processing (ATSIP), 2017, pp. 1–5.
    20. 20)
      • 55. Harel, J., Koch, C., Perona, P.: ‘Graph-based visual saliency’, Adv. Neural. Inf. Process. Syst., 2007, pp. 545–552.
    21. 21)
      • 10. Yuan, Y., Li, B., Meng, M.Q.-H.: ‘WCE abnormality detection based on saliency and adaptive locality-constrained linear coding’, IEEE Trans. Autom. Sci. Eng., 2016, 7, (10), pp. 737–741.
    22. 22)
      • 24. Suman, S., Hussin, F.A., Nicolas, W., et al: ‘Ulcer detection and classification of wireless capsule endoscopy images using RGB masking’, Adv. Sci. Lett., 2016, 22, (10), pp. 2764–2768.
    23. 23)
      • 13. Li, B., Meng, M.Q.-H., Lau, J.Y.W.: ‘Computer-aided small bowel tumor detection for capsule endoscopy’, Artif. Intell. Med., 2011, 52, (1), pp. 11–16.
    24. 24)
      • 6. Achanta, R., Estrada, F., Wils, P., et al: ‘Salient region detection and segmentation’ (Springer Berlin Heidelberg, Berlin, Heidelberg, 2008), pp. 66–75.
    25. 25)
      • 52. Seguí, S., Drozdzal, M., Vilariño, F., et al: ‘Categorization and segmentation of intestinal content frames for wireless capsule endoscopy’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (6), pp. 1341–1352.
    26. 26)
      • 1. Iddan, G., Meron, G., Glukhovsky, A., et al: ‘Wireless capsule endoscopy’, Nature, 2000, 405, (6785), pp. 405–417.
    27. 27)
      • 21. Koshy, N.E., Gopi, V.P.: ‘A new method for ulcer detection in endoscopic images’. 2015 2nd Int. Conf. on Electronics and Communication Systems (ICECS), IEEE, 2015, pp. 1725–1729.
    28. 28)
      • 2. Li, Z., Carter, D., Eliakim, R., et al: ‘The current main types of capsule endoscopy’. 2014, pp. 5–45.
    29. 29)
      • 37. Sivakumar, P., Kumar, B.M.: ‘A novel method to detect bleeding frame and region in wireless capsule endoscopy video’. Cluster Comput., 2018, pp. 1–7, https://doi.org/10.1007/s10586-017-1584-y.
    30. 30)
      • 29. Yuan, Y., Meng, M.Q.-H.: ‘Polyp classification based on bag of features and saliency in wireless capsule endoscopy’. 2014 IEEE Int. Conf. on Robotics and Automation (ICRA), 2014, pp. 3930–3935.
    31. 31)
      • 40. Xia, K.-j., Yin, H.-s., Wang, J.-q.: ‘A novel improved deep convolutional neural network model for medical image fusion’. Cluster Computing, 2018, pp. 1–13, https://doi.org/10.1007/s10586-018-2026-1.
    32. 32)
      • 14. Nawarathna, R., Oh, J., Muthukudage, J., et al: ‘Abnormal image detection in endoscopy videos using a filter bank and local binary patterns’, Neurocomputing, 2014, 144, pp. 70–91.
    33. 33)
      • 27. Yuan, Y., Li, B., Meng, M.Q.: ‘Bleeding frame and region detection in the wireless capsule endoscopy video’, IEEE J. Biomed. Health Inform., 2016, 20, (2), pp. 624–630.
    34. 34)
      • 41. Ponte, A., Pinho, R., Rodrigues, A., et al: ‘Evaluation and comparison of capsule endoscopy scores for assessment of inflammatory activity of small-bowel in Crohn's disease’, Gastroenterol. Hepatol., 2017, 41, (4), pp. 245–250.
    35. 35)
      • 50. Boreczky, J.S., Wilcox, L.D.: ‘A hidden Markov model framework for video segmentation using audio and image features’. Proc. of the 1998 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 1998, 1998, vol. 6, pp. 3741–3744.
    36. 36)
      • 48. Murugappan, V., Sabeenian, R.: ‘Texture based medical image classification by using multi-scale Gabor rotation-invariant local binary pattern (MGRLBP)’. Cluster Computing, 2017, pp. 1–14.
    37. 37)
      • 36. Charfi, S., Ansari, M.E.: ‘Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images’, Multimedia Tools Appl., 2018, 77, (3), pp. 4047–4064.
    38. 38)
      • 25. Chen, H., Wang, S., Ding, Y., et al: ‘Saliency-based bleeding localization for wireless capsule endoscopy diagnosis’, Int. J. Biomed. Imaging, 2017, 2017.
    39. 39)
      • 4. Buscaglia, J.M., Giday, S.A., Kantsevoy, S.V., et al: ‘Performance characteristics of the suspected blood indicator feature in capsule endoscopy according to indication for study’, Clin. Gastroenterol. Hepatol., 2008, 6, (3), pp. 298–301.
    40. 40)
      • 22. Ghosh, T., Das, A., Sayed, R.: ‘Automatic small intestinal ulcer detection in capsule endoscopy images’, Int. J. Sci. Eng. Res., 2016, 7, (10), pp. 737–741.
    41. 41)
      • 39. Seguí, S., Drozdzal, M., Pascual, G., et al: ‘Generic feature learning for wireless capsule endoscopy analysis’, Comput. Biol. Med., 2016, 79, pp. 163–172.
    42. 42)
      • 56. Itti, L., Koch, C., Niebur, E.: ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (11), pp. 1254–1259.
    43. 43)
      • 33. Hassan, A.R., Haque, M.A.: ‘Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos’, Comput. Methods Programs Biomed., 2015, 122, (3), pp. 341–353.
    44. 44)
      • 23. Souaidi, M., Abdelouahed, A.A., El Ansari, M.: ‘Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images’. Multimedia Tools Appl., 2018, pp. 1–18, https://doi.org/10.1007/s11042-018-6086-2.
    45. 45)
      • 49. Bosch, A., Zisserman, A., Munoz, X.: ‘Representing shape with a spatial pyramid kernel’. Proc. of the 6th ACM Int. Conf. on Image and Video Retrieval, CIVR ‘07, New York, NY, USA, ACM, 2007, pp. 401–408.
    46. 46)
      • 46. Achanta, R., Shaji, A., Smith, K., et al: ‘SLIC superpixels’. Technical report, 2010.
    47. 47)
      • 20. Yeh, J.-Y., Wu, T.-H., Tsai, W.-J.: ‘Bleeding and ulcer detection using wireless capsule endoscopy images’, J. Softw. Eng. Appl., 2014, 7, (5), p. 422.
    48. 48)
      • 32. Ghosh, T., Fattah, S.A., Wahid, K.A., et al: ‘Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video’, Comput. Biol. Med., 2018, 94, pp. 41–54.
    49. 49)
      • 11. Charisis, V.S., Hadjileontiadis, L.J., Liatsos, C.N., et al: ‘Capsule endoscopy image analysis using texture information from various colour models’, Comput. Meth. Prog. Bio., 2012, 107, (1), pp. 61–74.
    50. 50)
      • 30. El Ansari, M., Charfi, S.: ‘Computer-aided system for polyp detection in wireless capsule endoscopy images’. 2017 Int. Conf. on Wireless Networks and Mobile Communications (WINCOM), 2017.
    51. 51)
      • 15. Souaidi, M., Abdelouahad, A.A., El Ansari, M.: ‘A fully automated ulcer detection system for wireless capsule endoscopy images’. 2017 Int. Conf. on Advanced Technologies for Signal and Image Processing (ATSIP), 2017, pp. 1–6.
    52. 52)
      • 38. Jia, X., Meng, M.Q.: ‘A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images’. 38th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, FL, USA, August 16–20, 2016, pp. 639–642.
    53. 53)
      • 45. Zheng, Y., Yu, J., Kang, S.B., et al: ‘Single-image vignetting correction using radial gradient symmetry’. IEEE Conf. on Computer Vision and Pattern Recognition, 2008. CVPR 2008, 2008, pp. 1–8.
    54. 54)
      • 5. Hassan, M., Amin, M., Murtza, I., et al: ‘Robust hidden Markov model based intelligent blood vessel detection of fundus images’, Comput. Methods Programs Biomed., 2017, 151, pp. 193–201.
    55. 55)
      • 28. Yuan, Y., Li, B., Meng, M.Q.: ‘Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images’, IEEE Trans. Autom. Sci. Eng., 2016, 13, (2), pp. 529–535.
    56. 56)
      • 47. Ojala, T., Pietikäinen, M., Mäenpää, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971–987.
    57. 57)
      • 34. Iwahori, Y., Hattori, A., Adachi, Y., et al: ‘Automatic detection of polyp using Hessian filter and HOG features’, KES, 2015, 60, pp. 730–739.
    58. 58)
      • 53. W.E. Organization: ‘WEO clinical endoscopy atlas’. 1962.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6232
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6232
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address