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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 2))

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Abstract

In this paper, the new medical image management system is proposed. In this system, the system saves image data at any time and extracts feature quantity of each image automatically. At the same time, the information on feature quantity is stored in the metadata of the image. Since image processing is performed automatically, the users do not have burdens for adding feature information. To examine the validity of the proposed system, jpeg pictures which have Exchangeable image file format (Exif) data are stored and the image features are extracted. In the evaluation experiment, the experiment of system of operation is conducted and it is checked whether the system operates normally. At the same time, required time to extract features and write to the metadata is measure and evaluated.

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References

  1. Kosak, T., Miwa, K., Yonemura, Y., et al.: A clinicopathologic study on multiple gastric cancers with special reference to distal gestrectomy Cancer. Cancer 65, 2602–2605 (1990)

    Article  Google Scholar 

  2. Moertel, C.G., Gargen, J.A., Soule, E.D.: Multiple gastric cancers; review of the literature and study of 42 cases. Gastroenterology 32, 1095–1103 (1957)

    Google Scholar 

  3. Yamagiwa, H., Yoshimura, H., Matsuzaki, O., Ishihara, A.: Pathological study of multiple gastric carcinoma. Acta Pathol Jpn 30, 421–426 (1980)

    Google Scholar 

  4. Kurita, H.: A study of clinico-epidemiology on multiple gastric cancer. Nagoya Med. J. 22, 239–249 (1977)

    Google Scholar 

  5. Noguchi, Y., Ohta, H., Takagi, K., et al.: Synchronous multiple early gastric carcinoma: A study of 178 cases. World J. Surg. 9, 786–793 (1985)

    Article  Google Scholar 

  6. Pepe, M.S., Etzioni, R., Feng, Z., et al.: Phases of Biomarker Development for Early Detection of Cancer. Journal of the National Cancer Institute 93, 1054–1061 (2013)

    Article  Google Scholar 

  7. Henson, D.E., Srivastava, S., Kramer, B.S.: Molecular and genetic targets in early detection. Curr. Opin. Oncol. 11, 419–425 (1999)

    Article  Google Scholar 

  8. Srivastava, S., Kramer, B.S.: Early detection cancer research network. Lab Invest 80, 1147–1148 (2000)

    Article  Google Scholar 

  9. Greenwald, P.: New directions in cancer control. Johns Hopkins Med. J. 151, 209–213 (1982)

    Google Scholar 

  10. Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recognition 13, 3–16 (1981)

    Article  MathSciNet  Google Scholar 

  11. Strom, J., Cosman, P.C.: Medical image compression with lossless regions of interest. Signal Processing 59, 155–172 (1997)

    Article  Google Scholar 

  12. Mori, K., Urano, A., Hasegawa, J., et al.: Virtualized endoscope system – an application of virtual reality technology to diagnostic aid. IEICE Transactions on Information and Systems E79-D, 809–819 (1996)

    Google Scholar 

  13. Mori, K., Deguchi, D., Sugiyama, J., et al.: Tracking of a bronchoscope using epipolar geometry analysis and intensity-based image registration of real and virtual endoscopic images. In: Special Issue on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, vol. 6, pp. 321–336 (2002)

    Google Scholar 

  14. Hiroyasu, T., Uehori, K., Yamamoto, U., Tanaka, M.: Construction of an Interactive System Aims to Extract Expert Knowledge about the Condition Cultured Corneal Endothelial Cells 65, 1805–1810 (2013)

    Google Scholar 

  15. Choobineh, J., Vokurka, R.J., Vadi, L.: A prototype expert system for the evaluation and selection of potential suppliers 16, 106–127 (1980)

    Google Scholar 

  16. Kosak, T., Miwa, K., Yonemura, Y., et al.: Using extended file information (EXIF) file headers in digital evidence analysis. International Journal of Digital Evidence, Economic Crime Institute (ECI) 2, 1–5 (2004)

    Google Scholar 

  17. OpenCV: Open source Computer Vision library, http://opencv.org

  18. Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media Inc. (2008)

    Google Scholar 

  19. Viola, P., Jones, M.J.: Rapid Object Detection using a Boosted Cascade of Simple Features 1, 511–518 (2001)

    Google Scholar 

  20. Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection 1, 900–903 (2002)

    Google Scholar 

  21. Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.-Y.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

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Correspondence to Tomoyuki Hiroyasu .

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Hiroyasu, T., Nishimura, Y., Yamamoto, U. (2015). Medical Image Management System with Automatic Image Feature Tag Adding Functions. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_48

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  • DOI: https://doi.org/10.1007/978-3-319-13356-0_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13355-3

  • Online ISBN: 978-3-319-13356-0

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