Journal of Gastroenterology

, Volume 46, Issue 12, pp 1382–1390 | Cite as

Quantitative analysis of colorectal lesions observed on magnified endoscopy images

  • Keiichi Onji
  • Shigeto YoshidaEmail author
  • Shinji Tanaka
  • Rie Kawase
  • Yoshito Takemura
  • Shiro Oka
  • Toru Tamaki
  • Bisser Raytchev
  • Kazufumi Kaneda
  • Masaharu Yoshihara
  • Kazuaki Chayama
Original Article—Alimentary Tract



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.


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.


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.


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.


Magnifying chromoendoscopy Quantitative analysis Image analysis Colon cancer Colonoscopy 



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.


  1. 1.
    Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55:74–108.PubMedCrossRefGoogle Scholar
  2. 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.PubMedCrossRefGoogle Scholar
  3. 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.PubMedCrossRefGoogle Scholar
  4. 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.PubMedCrossRefGoogle Scholar
  5. 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.PubMedCrossRefGoogle Scholar
  6. 6.
    Tanaka S, Kaltenbach T, Chayama K, Soetikno R. High-magnification colonoscopy (with videos). Gastrointest Endosc. 2006;64:604–13.PubMedCrossRefGoogle Scholar
  7. 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.PubMedCrossRefGoogle Scholar
  8. 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.PubMedCrossRefGoogle Scholar
  9. 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.PubMedCrossRefGoogle Scholar
  10. 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.PubMedCrossRefGoogle Scholar
  11. 11.
    Kudo S, Rubio CA, Teixeira CR, Kashida H, Kogure E. Pit pattern in colorectal neoplasia: endoscopic magnifying view. Endoscopy. 2001;33:367–73.PubMedGoogle Scholar
  12. 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.PubMedCrossRefGoogle Scholar
  13. 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.PubMedCrossRefGoogle Scholar
  14. 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.PubMedCrossRefGoogle Scholar
  15. 15.
    Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–22.CrossRefGoogle Scholar
  16. 16.
    Haralick RM. Statistical and structural approaches to texture. Proc IEEE. 1979;67:786–804.CrossRefGoogle Scholar
  17. 17.
    Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061–9.PubMedCrossRefGoogle Scholar
  18. 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.PubMedCrossRefGoogle Scholar
  19. 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.PubMedCrossRefGoogle Scholar
  20. 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.Google Scholar
  21. 21.
    Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60:91–110.CrossRefGoogle Scholar
  22. 22.
    Carstensen JM. Description and simulation of visual texture. Technical report (Academic Dissertation), Technical University of Denmark. vol. 59; 1992. p. 9–74.Google Scholar
  23. 23.
    Siew LH, Hodgson RM, Wood EJ. Texture measures for carpet wear assessment. IEEE Trans Pattern Anal Mach Intell. 1988;10:92–105.CrossRefGoogle Scholar
  24. 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.PubMedCrossRefGoogle Scholar
  25. 25.
    Mir AH, Hanmandlu M, Tandon SN. Texture analysis of CT images. IEEE Eng Med Biol Mag. 1995;14:781–6.CrossRefGoogle Scholar
  26. 26.
    Vedaldi A, Fulkerson B. VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of ACM Multimedia; 2010. p. 1469–72.Google Scholar
  27. 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.Google Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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. 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. 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.CrossRefGoogle Scholar
  32. 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.PubMedCrossRefGoogle Scholar
  33. 33.
    Tung SY, Wu CS, Su MY. Magnifying colonoscopy in differentiating neoplastic from nonneoplastic colorectal lesions. Am J Gastroenterol. 2001;96:2628–32.PubMedCrossRefGoogle Scholar
  34. 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.PubMedCrossRefGoogle Scholar
  35. 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.PubMedGoogle Scholar
  36. 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.PubMedGoogle Scholar
  37. 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.PubMedGoogle Scholar
  38. 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.PubMedCrossRefGoogle Scholar
  39. 39.
    Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mac Intell. 2005;27:1615–30.CrossRefGoogle Scholar
  40. 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.Google Scholar
  41. 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.Google Scholar

Copyright information

© Springer 2011

Authors and Affiliations

  • Keiichi Onji
    • 1
  • Shigeto Yoshida
    • 2
    Email author
  • Shinji Tanaka
    • 2
  • Rie Kawase
    • 1
  • Yoshito Takemura
    • 1
  • Shiro Oka
    • 2
  • Toru Tamaki
    • 3
  • Bisser Raytchev
    • 3
  • Kazufumi Kaneda
    • 3
  • Masaharu Yoshihara
    • 4
  • Kazuaki Chayama
    • 1
  1. 1.Department of Medicine and Molecular Science, Graduate School of Biomedical SciencesHiroshima UniversityHiroshimaJapan
  2. 2.Department of EndoscopyHiroshima University HospitalHiroshimaJapan
  3. 3.Department of Information Engineering, Graduate School of EngineeringHiroshima UniversityHiroshimaJapan
  4. 4.Department of Health Service CenterHiroshima UniversityHiroshimaJapan

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