한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제14권12호
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  • Pages.191-199
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  • 2009
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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EM기반 관계기법을 이용한 의료영상 분석

Analysis of Medical Images Using EM-based Relationship Method

  • 김형일 (나사렛대학교 멀티미디어학과)
  • 발행 : 2009.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

의료영상에 대한 영상정보와 진단정보를 공유하는 환경으로 사용되는 의료영상 시스템은 효과적인 진단 보조 도구로 활용된다. 대규모 의료기관과 협력기관들은 통합 의료정보 시스템이 구축되어 영상정보와 진단정보를 공유할 수 있다. 그러나 통합 의료정보 시스템은 단순히 정보의 저장과 전송만을 제공한다. 이러한 문제점을 해결하고 진단 활동의 효율성을 높이기 위해서는 의료영상 분석 시스템이 필요하다. 본 논문에서 제안한 관계기법은 속성 생성을 위해 의료영상을 분석하고, 본 기법 하에 의료영상은 여러 개의 객체로 분할되며, 의료영상 속성들은 분할된 영상에서 추출된다. 추출된 속성들은 의료영상 분석을 위해 관계기법에 적용된다. 몇 가지 실험 결과를 통해 제안 기법의 효과를 확인하였다.

The integrated medical information system is an effective medical diagnosis assistance system which offers an environment in which medial images and diagnosis information can be shared. Because of the large-scale medical institutions and their cooperating organizations are operating the integrated medical information systems, they can share medical images and diagnosis information. However, this system can only stored and transmitted information without other functions. To resolve this problem and to enhance the efficiency of diagnostic activities, a medical image analysis system is necessary. In this paper, the proposed relationship method analyzes medical images for features generation. Under this method, the medical images have been segmented into several objects. The medical image features have been extracted from each segmented image. Then, extracted features were applied to the Relationship Method for medical image analysis. Several experimental results that show the effectiveness of the proposed method are also presented.

키워드

참고문헌

  1. S. S. Boochever, "HIS/RIS/PACS Integration: Getting to the Gold Standard," Radiology Management, Vol.26, No.3, 2004.
  2. X. Cao and H. K. Huang, "Current Status and Future Advances of Digital Radiography and PACS," IEEE Engineering in Medicine and Biology Magazine, Vol. 19, pp. 80-88, 2000. https://doi.org/10.1109/51.870234
  3. K. A. Johnson and J. A. Becker, "The Whole Brain Atlas," Harvard University Press, 1997.
  4. W. Hersh, H. Muller, J. Jensen, J. Yang, P. Gorman, and P. Ruch, "Advancing Biomedical Image Retrieval: Development and Analysis of a Test Collection," Journal of the American Medical Informatics, Vol. 13, No. 5, 2006.
  5. W. W. Chu, A. F. Cardenas, and R. K. Taira, "KMeD: A knowledge-based multimedia medical distributed database system," Information Systems, Vol. 20, No. 2, 1995.
  6. F. Sauer and M. Kabuka, "Multimedia Technology in the Radiology Department," In Proceedings of the Second ACM International Conference on Multimedia, 1994.
  7. H. Muller, N. Michoux, D. Bandon, and A. Geissbuhler, "A Riview of Content-based Image Retrieval Systems in Medical Applications-Clinical Benefits and Future Directions," International Journal of Medical Informatics, Vol. 73, 2004.
  8. M. C. Oliveira, W. Cirne, and P. M. de Azevedo Marques, "Towards Applying Content-based Image Retrieval in the Clinical Routine," Future Generation Computer System, Vol.23, 2007.
  9. T. M. Lehmann, M. O. Gould, C. Thies, B. Fischer, M. Keysers, D. Kohnen, H. Schubert, and B. B. Wein, "Content-based Retrieval in Picture Archiving and Communication Systems," In Proceedings of the SPIE Conference on Medical Imaging, Vol. 5033, 2003.
  10. 신용원, 구봉오, "지식 데이터베이스를 적용한 효율적인 세균 의료영상 검색 시스템의 구현," 한국컴퓨터정보학회논문지, 제10권, 제1호, 2005년 3월.
  11. S. K. Chang and A. Hsu, "Image Information Systems: Where do we go from here?," IEEE Transactions on Knowledge and Data Engineering, Vol. 4, No. 5, 1992.
  12. J. Kalpathy-Cramer and W. Hersh, "Image Modality based Classification and Annotation to Improved Medical Image Retrieval," In Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Heahth Systems, 2007.
  13. Y. Deng, B. S. Manjunath, C. Kenney, M. S. Moore, and H. Shin, "An Efficient Color Representation for Image Retrieval," IEEE Transaction on Image Processing. Vol. 10, No. 1, 2001.
  14. T. M. Lehmann, M. O. Guld, O. Thies, B. Fisher, K. Spitzer, D. Keysers, H. Ney, M. Kohnen, H. Schubert, and B. B. Wein, "Content-based Image Retrieval in Medical Applications," Methods of Information in Medicine. Vol. 43, 2004.
  15. J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. C. Jain, and C. Shu, "The Virage Image Search Engine: An Open Frame-Work for Image Management," In Symposium on Electronic Imaging: Science and Technology-Storage and Retrieval for Image and Video Databases IV, Vol. 2670, pp. 76-87, 1996.
  16. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, Petkovic, D. Steele, and P. Yanker, "Query by image Content: The QBIC System," IEEE Computer Special issue on Content Based Retrieval, Vol. 28. No. 9, 1995.
  17. R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Image Retrieval: Ideas, Influences, and Trends of the New Age," ACM Computing Surveys, Vol. 40, No. 2, 2008.
  18. V. E. Ogle and M. Stonebraker, "Chabot : Retrieval from a Relational Database of Images," In Proceedings of International Conference on IEEE Computer, Vol. 28, No. 9, 1995.
  19. G. J. McLachlan and T. Krishnan, "The EM Algorithm and Extensions," Wiley series in probability and statistics, John Wiley & Sons, New York, 1997.
  20. J. Salojarvi, K. Puolamaki, and S. Kaski, "Expectation Maximization Algorithms for Conditional Likelihoods," In Proceedings of the 22nd International Conference on Machine Learning, Vol. 119, pp. 752-759. 2005.
  21. Y. Bengio and Y. Gradvalet, "No Unbiased Estimator of the Variance of K-Fold Cross-Validation," Journal of Machine Learning Research, Vol. 5, pp. 1089-1105, 2004.
  22. M. W. Seeger, "Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods," The Journal of Machine Learning Research, Vol. 9, pp.1147-1178, 2008.