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Quantitative analysis of colorectal lesions observed on magnified endoscopy images

  • Original Article—Alimentary Tract
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Abstract

Background

Various surface mucosal pit patterns, as recognized by endoscopists, correlate with the histologic features of colorectal cancers. We investigated whether magnified endoscopy images of these pit patterns could be analyzed quantitatively and thus facilitate computer-aided diagnosis of colorectal lesions.

Methods

We applied both texture analysis and scale-invariant feature transform (SIFT) descriptors and discriminant analysis to magnified endoscopy images of 165 neoplastic colorectal lesions (pit patterns: type IIIL/IV, n = 44; type VI-mildly irregular, n = 36; type VI-severely irregular, n = 45; type VN, n = 40) [histologic findings: tubular adenoma (TA), n = 56; carcinoma with intramucosal or even scant submucosal invasion (M/SM-s), n = 52, carcinoma with massive submucosal invasion (SM-m), n = 57]. We analyzed differences in pit pattern values and corresponding histologic values to determine whether the values were diagnostically meaningful.

Results

Gray-level difference matrix (GLDM) inverse difference moment and spatial gray-level dependence matrix (SGLDM) local homogeneity values differed significantly between type IIIL/IV and type VN pit patterns. Values differed significantly for each analyzed feature between type IIIL/IV and type VI-severely irregular patterns and were high but descending for type IIIL/IV, type VI-mildly irregular, and type VI-severely irregular pit patterns (in that order). Similarly, texture analysis yielded high but descending values for TA, M/SM-s, and SM-m (in that order). Furthermore, SIFT descriptors and discriminant analysis yielded differences that were superior to those obtained by texture analyses.

Conclusions

Computer analysis of magnified endoscopy images for the diagnosis of colorectal lesions appears feasible. We anticipate further developments in the computer-aided diagnosis of pit patterns on magnified endoscopy images.

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References

  1. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55:74–108.

    Article  PubMed  Google Scholar 

  2. Matsuda T, Zhang M. Comparison of time trends in colorectal cancer mortality (1990–2006) in the world, from the WHO mortality database. Jpn J Clin Oncol. 2009;39:777–8.

    Article  PubMed  Google Scholar 

  3. Zavoral M, Suchanek S, Zavada F, Dusek L, Muzik J, Seifert B, et al. Colorectal cancer screening in Europe. World J Gastroenterol. 2009;15:5907–15.

    Article  PubMed  Google Scholar 

  4. Vogelstein B, Fearon ER, Hamilton SR, Kern SE, Preisinger AC, Leppert M, et al. Genetic alterations during colorectal-tumor development. N Engl J Med. 1988;319:525–32.

    Article  PubMed  CAS  Google Scholar 

  5. Winawer SJ, Zauber AG, Ho MN, O’Brien MJ, Gottlieb LS, Sternberg SS, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med. 1993;329:1977–81.

    Article  PubMed  CAS  Google Scholar 

  6. Tanaka S, Kaltenbach T, Chayama K, Soetikno R. High-magnification colonoscopy (with videos). Gastrointest Endosc. 2006;64:604–13.

    Article  PubMed  Google Scholar 

  7. Ueno H, Mochizuki H, Hashiguchi Y, Shimazaki H, Aida S, Hase K, et al. Risk factors for an adverse outcome in early invasive colorectal carcinoma. Gastroenterology. 2004;127:385–94.

    Article  PubMed  Google Scholar 

  8. Kitajima K, Fujimori T, Fujii S, Takeda J, Ohkura Y, Kawamata H, et al. Correlations between lymph node metastasis and depth of submucosal invasion in submucosal invasive colorectal carcinoma: a Japanese collaborative study. J Gastroenterol. 2004;39:534–43.

    Article  PubMed  Google Scholar 

  9. Kudo S, Hirota S, Nakajima T, Hosobe S, Kusaka H, Kobayashi T, et al. Colorectal tumours and pit pattern. J Clin Pathol. 1994;47:880–5.

    Article  PubMed  CAS  Google Scholar 

  10. Kudo S, Tamura S, Nakajima T, Yamano H, Kusaka H, Watanabe H. Diagnosis of colorectal tumorous lesions by magnifying endoscopy. Gastrointest Endosc. 1996;44:8–14.

    Article  PubMed  CAS  Google Scholar 

  11. Kudo S, Rubio CA, Teixeira CR, Kashida H, Kogure E. Pit pattern in colorectal neoplasia: endoscopic magnifying view. Endoscopy. 2001;33:367–73.

    PubMed  CAS  Google Scholar 

  12. Kanao H, Tanaka S, Oka S, Kaneko I, Yoshida S, Arihiro K, et al. Clinical significance of type VI pit pattern subclassification in determining the depth of invasion of colorectal neoplasms. World J Gastroenterol. 2008;14:211–7.

    Article  PubMed  Google Scholar 

  13. Matsumoto K, Nagahara A, Terai T, Ueyama H, Ritsuno H, Mori H, et al. Evaluation of new subclassification of type VI pit pattern for determining the depth and type of invasion of colorectal neoplasm. J Gastroenterol. 2011;46:31–8.

    Article  PubMed  Google Scholar 

  14. Onishi T, Tamura S, Kuratani Y, Onishi S, Yasuda N. Evaluation of the depth score of type V pit patterns in crypt orifices of colorectal neoplastic lesions. J Gastroenterol. 2008;43:291–7.

    Article  PubMed  Google Scholar 

  15. Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–22.

    Article  Google Scholar 

  16. Haralick RM. Statistical and structural approaches to texture. Proc IEEE. 1979;67:786–804.

    Article  Google Scholar 

  17. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061–9.

    Article  PubMed  CAS  Google Scholar 

  18. Chen CY, Chiou HJ, Chou SY, Chiou SY, Wang HK, Chou YH, et al. Computer-aided diagnosis of soft-tissue tumors using sonographic morphologic and texture features. Acad Radiol. 2009;16:1531–8.

    Article  PubMed  Google Scholar 

  19. Muldoon TJ, Thekkek N, Roblyer D, Maru D, Harpaz N, Potack J, et al. Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett’s esophagus. J Biomed Opt. 2010;15:026027.

    Article  PubMed  Google Scholar 

  20. Lowe DG. Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision; 1999. p. 1150–7.

  21. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60:91–110.

    Article  Google Scholar 

  22. Carstensen JM. Description and simulation of visual texture. Technical report (Academic Dissertation), Technical University of Denmark. vol. 59; 1992. p. 9–74.

  23. Siew LH, Hodgson RM, Wood EJ. Texture measures for carpet wear assessment. IEEE Trans Pattern Anal Mach Intell. 1988;10:92–105.

    Article  Google Scholar 

  24. Meier A, Farrow C, Harris BE, King GG, Jones A. Application of texture analysis to ventilation SPECT/CT data. Comput Med Imaging Graph. 2011;35:438–50.

    Article  PubMed  Google Scholar 

  25. Mir AH, Hanmandlu M, Tandon SN. Texture analysis of CT images. IEEE Eng Med Biol Mag. 1995;14:781–6.

    Article  Google Scholar 

  26. Vedaldi A, Fulkerson B. VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of ACM Multimedia; 2010. p. 1469–72.

  27. Bosch A, Zisserman A, Muñoz X. Image classification using random forests and ferns. In: Proceedings of the IEEE 11th international conference on computer vision, Rio de Janeiro, Brazil. vol. 23; 2007. p. 1–8.

  28. Cai D, He X, Han J. SRDA: an efficient algorithm for large-scale discriminant analysis. IEEE Trans Knowl Data Eng. 2008;20:1–12.

    Article  Google Scholar 

  29. Hamilton SR, Aaltonen LA. World Health Organization classification of tumours. Pathology and genetics of tumours of the digestive system. Lyon: IARC Press; 2000. p. 104–19.

    Google Scholar 

  30. Japanese Society for Cancer of the Colon and Rectum. General rules for clinical and pathological studies on cancer of the colon, rectum and anus. 7th ed. Tokyo: Kanehara Shuppan; 2006. (in Japanese).

    Google Scholar 

  31. Togashi K, Konishi F, Ishizuka T, Sato T, Senba S, Kanazawa K. Efficacy of magnifying endoscopy in the differential diagnosis of neoplastic and non-neoplastic polyps of the large bowel. Dis Colon Rectum. 1999;42:602–8.

    Article  Google Scholar 

  32. Kiesslich R, von Bergh M, Hahn M, Hermann G, Jung M. Chromoendoscopy with indigocarmine improves the detection of adenomatous and nonadenomatous lesions in the colon. Endoscopy. 2001;33:1001–6.

    Article  PubMed  CAS  Google Scholar 

  33. Tung SY, Wu CS, Su MY. Magnifying colonoscopy in differentiating neoplastic from nonneoplastic colorectal lesions. Am J Gastroenterol. 2001;96:2628–32.

    Article  PubMed  CAS  Google Scholar 

  34. Fu KI, Sano Y, Kato S, Fujii T, Nagashima F, Yoshino T, et al. Chromoendoscopy using indigo carmine dye spraying with magnifying observation is the most reliable method for differential diagnosis between non-neoplastic and neoplastic colorectal lesions: a prospective study. Endoscopy. 2004;36:1089–93.

    Article  PubMed  Google Scholar 

  35. Tanaka S, Haruma K, Oh-e H, Nagata S, Hirota Y, Furudoi A, et al. Conditions of curability after endoscopic resection for colorectal carcinoma with submucosally massive invasion. Oncol Rep. 2000;7:783–8.

    PubMed  CAS  Google Scholar 

  36. Tanaka S, Nagata S, Oka S, Kuwai T, Tamura T, Kitadai Y, et al. Determining depth of invasion by VN pit pattern analysis in submucosal colorectal carcinoma. Oncol Rep. 2002;9:1005–8.

    PubMed  Google Scholar 

  37. Nagata S, Tanaka S, Haruma K, Yoshihara M, Sumii K, Kajiyama G, et al. Pit pattern diagnosis of early colorectal carcinoma by magnifying colonoscopy: clinical and histological implications. Int J Oncol. 2000;16:927–34.

    PubMed  CAS  Google Scholar 

  38. Takemura Y, Yoshida S, Tanaka S, Onji K, Oka S, Tamaki T, et al. Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions. Gastrointest Endosc. 2010;72:1047–51.

    Article  PubMed  Google Scholar 

  39. Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mac Intell. 2005;27:1615–30.

    Article  Google Scholar 

  40. Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society conference on computer vision and pattern recognition. vol. 2; 2004. p. 506–13.

  41. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’05). vol. 1; 2005. p. 886–93.

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Acknowledgments

We would like to thank Jyunki Yoshimuta for his work with the calculation of images.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Medicine and Molecular Science, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

    Keiichi Onji, Rie Kawase, Yoshito Takemura & Kazuaki Chayama

  2. Department of Endoscopy, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

    Shigeto Yoshida, Shinji Tanaka & Shiro Oka

  3. Department of Information Engineering, Graduate School of Engineering, Hiroshima University, 1-4-1 Kagami-yama, Hiroshima, 739-8527, Japan

    Toru Tamaki, Bisser Raytchev & Kazufumi Kaneda

  4. Department of Health Service Center, Hiroshima University, 1-7-1 Kagami-yama, Hiroshima, 739-8514, Japan

    Masaharu Yoshihara

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  1. Keiichi Onji
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Correspondence to Shigeto Yoshida.

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Onji, K., Yoshida, S., Tanaka, S. et al. Quantitative analysis of colorectal lesions observed on magnified endoscopy images. J Gastroenterol 46, 1382–1390 (2011). https://doi.org/10.1007/s00535-011-0459-x

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  • DOI: https://doi.org/10.1007/s00535-011-0459-x

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