Analysis System of Endoscopic Image of Early Gastric Cancer

  • Kwang-Baek Kim
  • Sungshin Kim
  • Gwang-Ha Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


Gastric cancer is a great part of the cancer occurrence and the mortality from cancer in Korea, and the early detection of gastric cancer is very important in the treatment and convalescence. This paper, for the early detection of gastric cancer, proposes the analysis system of an endoscopic image of the stomach, which detects the abnormal region by using the change of color in the image and by providing the surface tissue information to the detector. While advanced inflammation and cancer may be easily detected, early inflammation and cancer are difficult to detect and requires more attention to be detected. This paper, at first, converts the endoscopic image to the image of the IHb(Index of Hemoglobin) model and removes noises incurred by illumination and, automatically detects the regions suspected as cancer and provides the related information to the detector, or provides the surface tissue information for the regions appointed by the detector. This paper does not intend to provide the final diagnosis of the detected abnormal regions as gastric cancer, but it intends to provide a supplementary mean to reduce the load and mistaken diagnosis of the detector, by automatically detecting the abnormal regions not easily detected by the human eye and this provides additional information for the diagnosis. The experiments using practical endoscopic images for performance evaluation showed that the proposed system is effective in the analysis of endoscopic image of the stomach.


Gastric Cancer Early Gastric Cancer Radial Basis Function Neural Network Abnormal Region Original Color 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Mitsui, A., Misumi, K., Murakami, A., Harada, K., Honmyo, U., Akagi, M.: Endoscopic diagnosis of minute, small, and flat early gastic cancers. Endoscopy 21, 159–164 (1989)Google Scholar
  2. 2.
    Honmyo, U., Mitsui, A., Murakami, A., Mizumoto, S., Yoshinaka, I., Maeda, M., et al.: Mechanisms producing color change in flat early gastric cancers. Endoscopy 29, 366–371 (1997)CrossRefGoogle Scholar
  3. 3.
    Kim, K.B., Yun, H.W.: A study on recognition of brochogenic cancer cell image using new physiological fuzzy neural networks. Japanese Journal of Medical Electronics and Biological Engineering 13(5), 39–43 (1999)Google Scholar
  4. 4.
    Kawano, S., Sato, N., Tsuji, S., et al.: Endoscopic blood flow analysis. Endoscopia Digestiva 1, 461–467 (1989)Google Scholar
  5. 5.
    Ogihara, T., Watanabe, H., Namihisa, M., Sato, N.: Display of mucosal blood flow function and color enhancement based on blood flow index (IHb color enhancement). Clinical Gastroenterology 12, 109–117 (1997)Google Scholar
  6. 6.
    Tsuji, S., Sato, N., Kawano, S.: Functional imaging for the analysis of the mucosal blood hemoglobin distribution using electronic endoscopy. Gastrointest. Endose. 34, 332–336 (1998)CrossRefGoogle Scholar
  7. 7.
    Forsyth, D.A., Ponce, J.: Computer Vision. Prentice Hall, Englewood Cliffs (2003) Google Scholar
  8. 8.
    Kim, K.-B., Kim, Y.-J., Oh, A.-s.: An intelligent system for passport recognition using enhanced RBF network. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 762–767. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Kim, K.B., Kim, Y.J.: Recognition of English calling cards by using enhanced fuzzy radial basis function neural networks. IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences E87-A(6), 1355–1362 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kwang-Baek Kim
    • 1
  • Sungshin Kim
    • 2
  • Gwang-Ha Kim
    • 3
  1. 1.Dept. of Computer EngineeringSilla UniversityKorea
  2. 2.School of Electrical EngineeringPusan National UniversityKorea
  3. 3.Dept. of Internal MedicinePusan National University College of MedicineKorea

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