DOI QR코드

DOI QR Code

비등방성 2차원 확산 기반 필터를 이용한 전산화단층영상 품질 개선

Image Quality Improvement in Computed Tomography by Using Anisotropic 2-Dimensional Diffusion Based Filter

  • 성열훈 (청주대학교 방사선학과)
  • 투고 : 2016.01.03
  • 심사 : 2016.01.30
  • 발행 : 2016.01.30

초록

본 연구에서는 비등방성 2차원 확산 기반 필터를 이용하여 전산화단층영상(computed tomography, CT)의 노이즈 제거와 공간분해능을 향상하고자 하였다. 실험은 4-채널 다중검출기 전산화단층영상기기(4-channel multi-detector computed tomography, MDCT)를 이용하였으며, CT 영상품질 평가를 위해 미국 의학물리학자협의회(american association of physicists in medicine, AAPM) CT 성능 평가용 팬톰을 사용하였다. X-선 조사 조건은 120 kVp, 100 mAs로 고정한 후 ultra-high resolution으로 10 mm 축 방향 스캔 하였다. 본 연구에서 제안한 비등방성 2차원 확산 기반 필터는 원 영상에 각 픽셀에 가중치 1.2를 곱하고 0.4% 히스토그램 스트레칭을 통해 영상의 대조도를 증가시킨 후 비등방성 2차원 확산 필터를 적용하였다. 그 결과, 공간분해능은 원 영상에서 0.75 mm까지 구분되었지만 제안한 비등방성 2차원 확산 기반 필터 영상에서는 0.40 mm까지 구분되었다. 원 영상의 노이즈는 46.0, 제안한 비등방성 2차원 확산 기반 필터 영상의 노이즈는 33.5로 27.2%가 감소하였다. 우리가 제안한 비등방성 2차원 확산 기반 필터는 CT의 노이즈 제거와 공간분해능을 향상시킬 수 있었다.

The purpose of this study was tried to remove the noise and improve the spatial resolution in the computed tomography (CT) by using anisotropic 2-dimensional (2D) diffusion based filter. We used 4-channel multi-detector CT and american association of physicists in medicine (AAPM) phantom was used for CT performance evaluation to evaluate the image quality. X-ray irradiation conditions for image acquisition was fixed at 120 kVp, 100 mAs and scanned 10 mm axis with ultra-high resolution. The improvement of anisotropic 2D diffusion filtering that we suggested firstly, increase the contrast of the image by using histogram stretching to the original image for 0.4%, and multiplying the individual pixels by 1.2 weight value, and applying the anisotropic diffusion filtering. As a result, we could distinguished five holes until 0.75 mm in the original image but, five holes until 0.40 mm in the image with improved anisotropic diffusion filter. The noise of the original image was 46.0, the noise of the image with improved anisotropic 2D diffusion filter was decreased to 33.5(27.2%). In conclusion improved anisotropic 2D diffusion filter that we proposed could remove the noise of the CT image and improve the spatial resolution.

키워드

참고문헌

  1. S. Diederich, D. Wormanns, W. Heindel, "Lung Cancer screening with low-dose CT," Europen Journal of Radiology, Vol. 45, No. 2, pp. 2-7, 2003. https://doi.org/10.1016/S0720-048X(02)00302-9
  2. S. J. Swensen, G. L. Aughenbaugh, L. R. Brown "High-resolution computed tomography of the lung," Mayo Clinic Proceedings, Vol. 64, No. 10, pp.1284-1294, 1989. https://doi.org/10.1016/S0025-6196(12)61292-0
  3. C. J. Bergin, N. L. Muller "CT in the diagnosis of interstitial lung disease," American Journal of Roentgenology, Vol. 145, No. 3, pp. 9-15, 1985. https://doi.org/10.2214/ajr.145.1.9
  4. B. H. Lee, Y. J. Hwang, G. Hur, S. Y. Kim, J. Y. Lee, Y. H. Kim, "Multidetector computed tomography (MDCT) images with soft tissue and bone algorithm reconstruction in head and facial trauma," Journal of Clinical Neuroscience, Vol. 18, Issue 7, pp. 899-901, 2011. https://doi.org/10.1016/j.jocn.2010.10.019
  5. K. S. Chon, "Noise Properties for Filtered Back Projection in CT Reconstruction," Journal of the Korea Society of Radiology, Vol. 8, No. 6, pp. 357-364, 2014. https://doi.org/10.7742/jksr.2014.8.6.357
  6. G. N. Ramachandran, A.V. Lakhshminarayana, "Three-dimensional reconstruction from radiographs and electron micrographs: Application of convolution instead of Fourier transform," Proceedings of the National Academy of Sciences of the United States of America Vol. 68, No. 9, pp. 2236-2240, 1971. https://doi.org/10.1073/pnas.68.9.2236
  7. B. Kang, "Quantitative evaluation of CT artifact elimination with various Cut-off frequency of Hann filter," Journal of the Korea Society of Radiology, Vol. 2, No. 3, pp. 5-9, 2008.
  8. P. Perona, J. Malik, "Scale-Space and Edge Detection Using Anisotropic Diffusion", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 12 No. 7, pp. 629-639, 1990.
  9. G. Y. Go, D. H. Ju, D. H. Yum, D. Y. Kim, "Inverse halftoning Using Anisotropic diffusion and Edge map", The Korea Institute of Signal Processing and Systems, Vol. 1 No. 2, pp. 81-84, 2000.
  10. H. R. Ji, H. Hong, "Automatic detection of foreign body through template matching in industrial CT volume data," Journal of Korea Multimedia Society, Vol. 16, No. 12, pp. 1376-1384, 2013. https://doi.org/10.9717/kmms.2013.16.12.1376
  11. S. Rodtook, Y. Rangsanseri, "Adaptive Thresholding of Document Image Based on Laplacian Sign," International Conference on Information Technology : Coding and Computin, Las Vegas, April, 2001.
  12. A. B. Wolbarst, Physics of radiology, 2nd edition, Medical Physics Publishing Co., 2005.
  13. C. G. Rafael, E. W. Richard, Digital Image Processing, 2nd edition Addison Wesley, 2001.
  14. T. Huang, G. Yang, G. Tang, "A fast two-dimensional median filtering algorithm," IEEE Trans. Acount., Speech. Signal Processing, Vol. 27, No. 1, pp. 13-18, 1979. https://doi.org/10.1109/TASSP.1979.1163188
  15. M. J. Park, S. B. Lee, "Improvement of Angiogram Quality Using by High Pass Filter," Journal of the Korea Society of Radiology, Vol. 8, No. 6, pp. 301-307, 2014. https://doi.org/10.7742/jksr.2014.8.6.301
  16. Y. H. Seoung, H. J. Park, H. B. Kang, "Wavelet-based Noise reduction filter for 3-Dimensional Computed Tomography Brain angiography," Korean Institute of Information Scientists and Engineers, Vol. 32 No. 2, pp. 859-861, 2005.