Comparison of DICOM images and various types of images

DICOM 영상과 다양한 형식의 영상 비교

  • Kim, Ji-yul (Dept. of Radiology. Daewoo general hospital) ;
  • Ko, Seong-Jin (Dept. of Radiological Science, Catholic University of Pusan)
  • 김지율 (대우의료재단 대우병원 영상의학과) ;
  • 고성진 (부산가톨릭대학교 보건과학대학 방사선학과)
  • Received : 2017.05.03
  • Accepted : 2017.06.03
  • Published : 2017.12.31

Abstract

In this study, the original medical image, DICOM file, was converted into TIFF, BITMAP, GIF, JPEG image file, and then the conversion loss ratio according to the image compression and conversion process was quantitatively evaluated using Origin pro and ICY image analysis program. As the evaluation method, 50% MTF, structural similarity index, MSE, RMSE, maximum signal - to - noise ratio and so on were evaluated. The TIFF image file showed the same result as DICOM image in all experimental groups, Image file format. In this study, we propose a new method for evaluating the quality of digital images by applying original evaluation program such as Origin pro or ICY medical image analysis program. Is expected to be used as research data in the field of medical image processing, and TIFF image file showing the same result as DICOM image in the basic research field using digital medical image and evaluation program that does not support DICOM file Therefore, it is believed that it will help to secure reliability in digital medical image processing research using image file.

본 연구에서는 원본 의료영상인 DICOM 파일을 TIFF, BITMAP, GIF, JPEG 이미지 파일로 변환한 후 Origin pro와 ICY 영상분석 프로그램을 이용하여 영상의 압축 및 변환과정에 따른 변환 손실율을 정량적으로 평가를 하고자 하였다. 평가 방법으로는 50% MTF, 구조적 유사지수, MSE, RMSE, 최대 신호대 잡음비 등을 실험을 통하여 평가하였으며, TIFF 이미지 파일의 경우 모든 실험군에서 DICOM 영상과 동일한 결과 값을 나타내어 DICOM 영상과 동일 하거나 가장 유사한 이미지 파일 형식이라고 판단하였다. 그리고 JPEG 이미지 파일의 화질의 손실 및 왜곡의 정도가 가장 심한 결과로 나타났다, 본 연구는 Origin pro나 ICY 의료영상 분석 프로그램과 같은 독창적인 평가 프로그램을 적용하여 이후의 디지털 의료영상 기초 연구분야에서 본 논문의 평가 방법이 의료 영상 처리 분야의 연구 자료로 활용될 것으로 기대되며, DICOM 파일을 지원하지 않는 디지털 의료영상 및 평가 프로그램을 이용한 기초 연구분야에서 DICOM 영상과 동일한 결과를 나타내는 TIFF 이미지 파일을 기준으로 제시하여, 이미지 파일을 이용한 디지털 의료영상처리 연구 분야에서 신뢰성을 확보하는데 도움이 될 것으로 추론된다.

Keywords

References

  1. Jong Hyo Kim "Medical Image Processing System" The institute of Electronics and information Engineers, 2013, pp. 54-59.
  2. Sang Cheol Park, Myung Eun Lee "Machine Learning for Medical Image Analysis" Korean Institute of Information Scientists Engineers, vol. 39, no. 3, 2012.
  3. Bo sun Kang "Development of Image Quality Evaluation Program for Digital Diagnostic Radiography" KOREAN SOCIETY OF RADIOLOGICAL SCIENCE, vol 2, no. 2, pp. 5-10, 2008.
  4. Eun Hyoung Chu, Mu Hun Park "Implement of Integrated Compression System of Medical Images" Graduate School of Changwon National University, 2002, pp. 14-15.
  5. Korea Ministry of Government Legislation, http://www.law.go.kr/
  6. Soon Mu Kwon "Change of Image Quality within Compression of AAPM CT Performance Phantom Image Using JPEG2000 in PACS" Department of Radiological Science, The Graduate School of Catholic University of Daegu, 2012.
  7. Jae ho Jeoung, Eun su Kim "The Research on Compression Image Quality of Full Field Digital Mammography on PACS Environment" The Korea Society of Radiology, vol. 8, no. 4, pp. 147-153, 2014. https://doi.org/10.7742/jksr.2014.8.4.147
  8. Jung Eun Woo, Yong Geum Lee, Seok Hwan Bae, Yong Gwon Kim "An Evaluation Method of X-ray Imaging System Resolution for Non-Engineers" KOREAN SOCIETY OF RADIOLOGICAL SCIENCE, vol. 35, no. 4, pp. 309-314, 2012.
  9. Ki Won Kim, Jung Whan Min et al "Comparison Study on CNR and SNR of Thoracic Spine Lateral Radiograph", KOREAN SOCIETY OF RADIOLOGICAL SCIENCE, vol. 36, no. 4, pp. 273-280, 2013.
  10. Han bean Youn, Ho sang Jeon, Dong hyun Kim et al "Feasibility of Automated Detection of Inter-fractional Deviation in Patient Positioning Using Structural Similarity Index : Preliminary Results" PROGRESS in MEDICAL PHYSICS, vol. 26, no. 4, pp. 258-266, 2015. https://doi.org/10.14316/pmp.2015.26.4.258
  11. Hyun Chul CHO, Kwoan Ho Lee "Fault Detection Algorithm of Photovoltaic Power Systems using Stochastic Decision Making Approach" The Korea Institute of Signal Processing and System, vol. 12, no. 3 pp. 212 -216, 2011.
  12. Sung sun Noh "A Study on Automated Quantitative Analysis in Evaluation of Low Contrastand Spatial Resolution Imagesusinga CT Standard Phantom, Department of Medical Image Engineering" Graduate School of Bio-Medical Science, Korea University, 2014.
  13. Sung Sun Noh, Hyo Sik Um, and Ho Chul Kim "Development of Automatized Quantitative Analysis Method in CT Images Evaluation using AAPM Phantom" Journal of The Institute of Electronics and Information Engineers, vol. 51, no. 12, pp. 163-173. 2014. https://doi.org/10.5573/IEIE.2014.51.12.163
  14. IBA dosimety, http://www.iba-dosimetry.com/complete-solutions/medical-imaging?q=node/596.
  15. OriginLab Korea, http://www.originkorea.com/index.php/2016-04-29-08-58-29/origin.
  16. ICY Institut pasteur, http://icy.bioimageanalysis.org.
  17. MTF measurement, http://blog.naver.com/y4769/220131955645.
  18. Jincheol Park, Sang hoon Lee "Structural Similarity Based Video Quality Metric using Human Visual System" Journal of broadcast engineering. vol. 14, no. 1, pp. 36-43, 2009. https://doi.org/10.5909/JBE.2009.14.1.36
  19. ICY tool : http://blog.naver.com/y4769/220505513170
  20. Rui Wang, Wei Yu, Runze Wu et al "Improved Image Quality in Dual Energy Abdominal CT: Comparison of Iterative Reconstruction in Image Space and Filtered Back Projection Reconstruction" American Journal of Roentgenology, vol. 199, no. 2, pp. 402-406, 2012. https://doi.org/10.2214/AJR.11.7159
  21. Ki Won Kim, Kwan Woo Choi, Hoi Woun Jeong, Seo Goo Jang et al "Evaluation of the Modulation Transfer Function for Computed Tomography by Using American Association Physics Medicine Phantom" Radiological Science and Technology, vol. 39, no. 2, pp. 193-198, 2016. https://doi.org/10.17946/JRST.2016.39.2.09