Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Image Quality Enhancement for Chest X-ray images

흉부 엑스레이 영상을 위한 화질 개선 알고리즘

  • Received : 2015.06.17
  • Accepted : 2015.09.24
  • Published : 2015.10.25
Download PDF
⟨ Previous Next ⟩

Abstract

The initial X-ray images obtained from a digital X-ray machine have a wide data range and uneven brightness level than normal images. In particular, in Chest X-ray images, it is necessary to improve naturally all of the parts such as ribs, spine, tissue, etc. These X-ray images can not be improved enough from conventional image quality enhancement algorithms because their characteristics are different from ordinary images'. This paper proposes to eliminate unnecessary background from an input image and expand the histogram range of the image. Then, we adjust the weight per frequency band of the image for improvement of contrast and sharpness. Finally, jointly taking the advantages of global contrast enhancement and local contrast enhancement methods we obtain an improved X-ray image suitable for effective diagnosis in comparison with the existing methods. Experimental results show quantitatively that the proposed algorithm provides better X-ray images in terms of the discrete entropy and saturation than the previous works.

디지털 엑스레이 기기로부터 처음 획득된 엑스레이 영상은 데이터 범위가 일반 영상에 비해 넓고 밝기 레벨이 고르지 못하다. 특히 흉부 엑스레이 영상의 경우 다양한 이유로 촬영하기 때문에 갈비뼈와 혈관, 척추 뼈 등 특성이 다른 모든 부위들을 자연스럽게 개선할 필요가 있다. 이러한 엑스레이 영상의 경우 일반 영상과 특성이 다르기 때문에 기존의 화질 개선 알고리즘으로는 진단에 적합한 화질을 얻을 수 없다. 따라서 본 논문은 특정 밝기에 밀집된 정보들의 히스토그램 범위를 확장시키고, 주파수 대역 별 가중치 조절을 통한 선명도 개선 및 고주파 성분의 특성을 이용한 영상 융합 기법을 통해 최종적으로 영상의 대비를 적절하게 개선하는 흉부 엑스레이 영상용 화질 개선 방법을 제안한다. 또한 기존의 기법들과 비교하여 흉부 엑스레이 영상을 보다 자연스럽게 개선하는 것을 확인하고 discrete entropy와 saturation을 통해 정량적 평과 결과를 보인다.

Keywords

References

  1. P. J. Burt and E. H. Adelson, "The Laplacian pyramid as a compact image code," IEEE Trans. Communications, vol. com-31, no. 4, pp. 532-540, April 1983.
  2. E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, "Pyramid methods in image processing," RCA Engineer, vol. 29, no. 6, pp. 33-41, Nov/Dec. 1984.
  3. S. Paris, S. W. Hasinoff, and J. Kautz, "Local Laplacian filters: edge-aware image processing with a Laplacian pyramid," ACM Trans. Graphics, 2011.
  4. H. Demirel, C. Ozcinar, and G. Anbarjafari, "Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition," IEEE Geoscience and Remote sensing, vol. 7, no. 2, pp. 333-337, April 2010. https://doi.org/10.1109/LGRS.2009.2034873
  5. J. L. Starck, F. Murtagh, E. J. Candes, and D. L. Donoho, "Gray and color image contrast enhancement by the curvelet transform," IEEE Trans. Image Processing, vol. 12, no. 6, pp. 706-717, June 2003. https://doi.org/10.1109/TIP.2003.813140
  6. L. Tao and V. K. Asari, "Modified luminance based MSR for fast and efficient image enhancement," in Proc. IEEE AIPR, 2003.
  7. B. Sun, W. Chen, H. Li, W. Tao, and J. Li, "Modified luminance based adaptive MSR," in Proc. IEEE ICIG, 2007.
  8. J. H. Jang, B. Choi, S. D. Kim, and J. B. Ra, "Sub-band decomposed multiscale retinex with space varying gain," Proc. IEEE Int. Conf. Image Process, pp. 3168-3171, 2008.
  9. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. Reading, MA: Addison-Wesley, 1992.
  10. J. B. Zimmerman, S. M. Pizer, "Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement," Proc. 25th Fall Symposia - Imaging, 17-22, Nov. 1985, Soc. of Photographic Scientists and Engineers, pp. 189-190, 1985.
  11. Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Trans. Consumer Electron, vol. 41, no. 1, pp. 1-8, 1997.
  12. H. Ibrahim and N. S. P. Kong, "Brightness preserving dynamic histogram equalization for image contrast enhancement," IEEE Trans. Consumer Electron, vol. 53, no. 4, pp. 1752-1758, Nov. 2007. https://doi.org/10.1109/TCE.2007.4429280
  13. Huang et al, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution," IEEE Trans. Image Processing, vol. 22, no. 3, pp. 1032-1041, Mar. 2013. https://doi.org/10.1109/TIP.2012.2226047
  14. K. Zuiderveld, "Contrast limited adaptive histogram equalization," in Graphics Gems IV, P. Heckbert, Ed. New York: Academic, 1994, ch. VIII.5, pp. 474-485.
  15. J. Y. Kim, L. S. Kim, and S. H. Hwang, "An advanced contrast enhancement using partially overlapped sub-block histogram equalization," IEEE Trans. Circuits and Systems for Video Technology, vol. 11, no. 4, pp. 475-484, April 2011.
  16. T. Mertens, J. Kautz, and F. V. Reeth, "Exposure fusion: a simple and practical alternative to high dynamic range photography," Comput. Graph Forum, vol. 28, no. 1, pp. 161-171, Mar. 2009.
  17. A. Saleem, A. Beghdadi, and B. Boashash, "Image fusion-based contrast enhancement," EURASIP Journal on Image and Video Processing, May 2012.
  18. J. W. Park, and B. C. Song, "Body and ROI segmentation algorithms for Chest X-ray images," submitted to Journal of the Institute of Electronics and Information Engineers, August 2015.
  19. T. Qiu, A. Wang, N. Yu, and A. Song, "LLSURE: local linear SURE-based edge-preserving image filtering," IEEE Trans. Image Processing, vol. 22, no. 1, pp. 80-90, Jan. 2013. https://doi.org/10.1109/TIP.2012.2214052
  20. S. S. Agaian, B. Silver, and K. A. Panetta, "Transform coefficient histogram-based image enhancement algorithms using contrast entropy," IEEE Trans. Image Processing, vol. 16, no. 3, pp. 741-758, Mar. 2007. https://doi.org/10.1109/TIP.2006.888338