DOI QR코드

DOI QR Code

Compression of Medical Examination Data Based on Modified Gamma-Coding

수정된 감마 코딩 기반 의료 검진 데이터 압축

  • Ku, Dong Youn (Dept. of Computer Science & Engineering, Dongguk University-Seoul) ;
  • Park, Jae Wook (Dept. of Computer Science & Engineering, Dongguk University-Seoul) ;
  • Lee, Yong Kyu (Dept. of Computer Science & Engineering, Dongguk University-Seoul)
  • 구동윤 (동국대학교 컴퓨터공학과-서울) ;
  • 박재욱 (동국대학교 컴퓨터공학과-서울) ;
  • 이용규 (동국대학교 컴퓨터공학과-서울)
  • Received : 2013.06.18
  • Accepted : 2013.12.09
  • Published : 2014.02.28

Abstract

According to the development of medical information systems, shortened examination time per patient could increase the number of treatments, resulting in the rapid growth of the amount of medical data. Studies on how to efficiently compress and store medical text data of increasing patients are in progress. However, previous methods have the shortcoming of compressing medical text data as it is, resulting in low compression rate. This research tries to overcome the problem by using the gamma coding method which enables compression in bit unit. We propose a new compression scheme which encodes the deviations between measured values and normal range values. Furthermore, we suggest to use the previous value with the least deviation from the measurement as the standard value to encode that deviation. Even though the suggested methods are simple, they have high compression rates. Through performance evaluation, we show that the suggested methods are more efficient than the previous methods.

의료 정보 시스템의 발달로 인해 환자들의 진료 시간이 짧아지고 그에 따라 진료 받는 환자들의 수가 증가하여 진료 데이터 양이 급속도로 증가하고 있으며, 증가하는 환자들의 데이터를 효율적으로 저장, 관리하기 위해 다양한 압축 방법에 대한 연구가 진행 중이다. 그러나 기존 방법들은 측정값을 원시 데이터로 압축하여 저장하기 때문에 압축률이 떨어지는 단점이 있다. 이러한 문제를 해결하기 위해 본 논문에서는 비트 단위로 압축 가능한 감마 코딩 기법을 이용하여 측정값과 정상범위 값과의 편차를 부호화하여 압축하는 방법을 제안한다. 또한 측정값과 편차가 가장 작은 과거 데이터를 기준치로 삼아 그 편차를 부호화하여 압축하는 방법을 제안한다. 제안하는 방법은 매우 간단하며 편차를 압축하기 때문에 기존 방법보다 압축률이 높은 장점이 있다. 성능평가를 통하여 제안한 방법이 기존 압축방법보다 우수하다는 것을 검증한다.

Keywords

References

  1. Weehyuk Yu, Jongil Jeong, Dongkyoo Shin and Dongil Shin, "Compress transmission of XML-based Clinical Document," Proceedings of Korean Insitute of Information Scientists and Engineers, Vol. 32, No. 2, pp. 250-252, May. 2005.
  2. Myeong-Chan Kim, Yong-Taek Jeong, Tae-Sung Yoon and Young Huh, "New Lossless Compression Method Using Direction Block," Proceedings of Korea Multimedia Society, pp. 206-211, Nov. 1999.
  3. Hyung-Ju Cho and Chin-Wan Chung, "An Efficient Compression Method for Multi-dimensional Index Structures," Journal of Korean Insitute of Information Scientists and Engineers, Vol. 30, No. 5, pp. 429-437, Oct. 2003.
  4. Jeuyoung kim, Yoonhee Kim and Chan Hyun Youn, "Web service based Distributed Medical Data Management," Journal of Korean Insitute of Information Scientists and Engineers, Vol. 34, No. 1(B), pp. 339-343, June. 2007.
  5. Dong Youn Ku, Jae Wook Park and Yong Kye Lee, "Compression Methods for Medical Examination Data," Proceedings of Korea Multimedia Society, Vol. 15, No. 2, pp. 41-44, Nov. 2012.
  6. J.S. Lee, O.S. Kwon, J.Y. Koo, Y.H. Han and S.H. Hong, "A Lossless Medical Image Compression Using Variable Block," Journal of biomedical engineering research, Vol. 19, No. 4, pp. 361-367, 1998.
  7. Jae-Sung Jung and Chang-Hun Lee, "An Efficient Medical Image Compression Considering Brain CT Images with Bilateral Symmetry," Journal of the Webcasting, Internet and Telecommunication, Vol. 12, No. 5, pp. 39-54, 2012. https://doi.org/10.7236/JIWIT.2012.12.5.39
  8. Guy E. Belloch, "Introduction to Data Compression," Carnegie Mellon University press, pp. 25-34, Sept. 2010.
  9. Christopher D. Manning and Prabhakar Raghavan, "Introduction to Information Retrieval," Cambridge University Press, pp. 96-100, June. 2008.
  10. Ziv, J. and Lempel A., "Compression of Individual sequences via Variable-Rate Coding," IEEE Transaction on Information Theory, Vol. 24, No. 5, pp. 530-536, Sept. 1978. https://doi.org/10.1109/TIT.1978.1055934
  11. Serkan Eryilmaz, "Mean success run length," Journal of the Korean statistical society, Vol. 38, No. 1, pp. 65-71, Mar. 2009. https://doi.org/10.1016/j.jkss.2008.07.002
  12. Lee D. Davisson, "Universal Noiselss Coding," IEEE Transactions on Information Theory, Vol. IT-19, No. 6, pp. 783-795, Nov. 1973.
  13. Ziv, J. and Lempel, A., "A Universal Algorithm for Data Compression," IEEE Transaction on Information Theory, Vol. 23, No. 3, pp. 337-343, May. 1977. https://doi.org/10.1109/TIT.1977.1055714
  14. Debra A. Lelewer and Daniel S. Hirschberg, "Data Compression," ACM Computing Surveys, Vol. 19, No. 3, Sept. 1987.
  15. E. Yang, A. Kaltchenko and J.C. Kieffer, "Universal Lossless Data Compression With Side Information by Using a Conditional MPM Grammar Transform," IEEE Transaction on Information Theory, Vol. 47, No. 6, Sept. 2001.
  16. J.C. Kieffer and E. Yang, "Structured Grammar-Based codes for universal lossless data compression," Communication in Information and Systems, Vol. 2, No. 1, pp. 29-52, June. 2002. https://doi.org/10.4310/CIS.2002.v2.n1.a2
  17. Khalid Sayood, "Introduction to Data Compression," Morgan Kaufmann Publishers, Inc. pp. 25-283, 1996.
  18. Welch T., "A Technique for High Performance Data Compression," IEEE Computer, Vol. 17, No. 6, pp. 8-19, 1984.
  19. LA Koyrakh, "Data Compression for Implantable Medical Devices," IEEE Computers in Cardiology, 2008, pp. 417-420, Sept. 2008.
  20. Milanova, M., Kountchev, R., Todorov, V. and Kountcheva R., "New Method for Lossless Compression of Medical Records," IEEE ISSPIT 2008, pp. 23-28, Dec. 2008.
  21. Jung-Yeon Park, "A Study on the Network QoS Management for Telemedicine Service based on Internet," Journal of The Korea Society of Computer and Information, Vol. 7, No. 4, pp. 24-32, Dec. 2002.
  22. Soon-Hyoung Joung and Jong-Ryeol Park, "Study on Telemedicine system in Medical Law," Journal of The Korea Society of Computer and Information, Vol. 17, No. 12, pp. 241-249, Dec. 2012. https://doi.org/10.9708/jksci/2012.17.12.241