디지털콘텐츠학회 논문지 (Journal of Digital Contents Society)

  • 제12권2호
  • /
  • Pages.151-156
  • /
  • 2011
  • /
  • 1598-2009(pISSN)
  • /
  • 2287-738X(eISSN)
한국디지털콘텐츠학회 (Digital Contents Society)
권호 표지
DOI QR코드

DOI QR Code

위장 CT 검사에서 Ray-sum 기법과 SSD(Shaded Surface Display) 기법의 유용성 분석

The usability analysis of the Ray-sum technique and SSD (Shaded Surface display) technique in stomach CT Scan

  • 김현주 (순천향대학교 부천병원 영상의학과) ;
  • 조재환 (경산1대학 방사선과) ;
  • 송훈 (순천향대학교 부천병원 영상의학과)
  • 투고 : 2011.04.26
  • 심사 : 2011.05.25
  • 발행 : 2011.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

CT검사 후 재구성 영상 처리 기법인 Ray-sum 기법과 Shaded Surface Display(이하 SSD)기법을 분석 및 영상평가를 하여 위암 환자의 입체적인 정보 제공의 유용성 여부를 확인하고자 하였다. 위암환자 20명을 대상으로 64-MDCT를 이용하여 raw data(원시데이터)를 획득한 후 영상 재구성 처리를 하였다. 분석 결과 Ray-sum과 SSD재구성 영상모두 해부학적 구조를 정확히 묘사하는 것으로 평가 받았고, 영상의 정확도 평가에서 Ray-sum, SSD재구성 영상 대부분에서 병변의 위치가 위장 내시경과 일치하였으며 6cm이상에서 오차가 더 있음을 알 수 있었다. 또한 병변에 대한 영상판독결과와 내시경 및 병리학적 소견의 일치도가 높음을 알 수 있었다.

The analysis and image evaluation the Ray-sum technique and Shaded Surface Display (under SSD) technique which is the reconstruction image processing technique after the CT scan was evaluated and the usability of the three-dimensional information offering was confirmed in the patient with stomach cancer. After obtaining the raw data by using 64-MDCT in 20 patient with stomach cancers, the image reconstruction processing was done. It was evaluated to describe accurately the analyzed result Ray-sum and SSD reconstruction image everyone anatomical structure. In the precision estimation of the image, the lesion location could coincide in the Ray-sum and SSD reconstruction image majority with the gastro fiberscope and we can know than the gastro fiberscope over 6cm that there was the error. In addition, We could know that degree of accordance of the results of the image interpretation about the lesion and endoscope and pathological opinion were high.

키워드

참고문헌

  1. J. K. Udupa and G. T. Hermen, "3D Imaging in Medicine," CRC Press, 1991.
  2. H. E. Burdick, "Digital Imaging theory and application," McGraw_Hill, inc. 1997.
  3. S. E. Umbaugh, "Computer Vision and Image processing : A Practical Approach Using CVIPtools," Prentice Hall PTR, 1998.
  4. D. H. BALLARD and C. M. BROWN "Computer Vision," Prentice Hall, 1982.
  5. J. R. Parker, "Algorithm for image processing and Computer Vision," John Wiley and Sons, inc. 1997.
  6. M. J. CARLOTTO, "Histogram Analysis Using a Scale Space Approach," IEEE Transaction on PAMI, Vol. 30, No. 1, pp 121-129, 1987.
  7. S. H. Yoo, J. S. Cho, S. M. Noh, K. S. Shin, J. W. Park, "Advanced Liver Segmentation by Using Pixel Ratio in Abdominal CT Image" 2000 International Technical Conference on Circuits/ Systems, Computers and Communications, Vol. 1, No. 1, pp. 39-42, 2000.
  8. Kurtz RC, Sherlock P. The diagnosis of gastric cancer. Semin Oncol. Vol. 12, No. 1 pp. 11-18. 1985.
  9. Kim SH, Han JK, Lee KH, Chung JW, Yang HK, Choi BI. Computed tomography gastrography withvo lume-rendering technique: correction with double con trast barium study and conventional gatrography. J Computed Assist Tomography Vol. 27, No. 1, pp. 140 -149, 2003. https://doi.org/10.1097/00004728-200303000-00006
  10. Kim JH, Park SH, Hong HS, Auh YH. CT gastrogrphy. Abdomen Imaging Vol. 30, No. 1, pp. 509-517, 2005. https://doi.org/10.1007/s00261-004-0282-4