Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지

Detection of Retinal Vessels of Fundus Photograph Using Hessian Algorithm

안저 영상에서 헤이지안 알고리즘을 이용한 혈관 검출

  • 강호철 (국립암센터 의공학연구과) ;
  • 김광기 (국립암센터 의공학연구과) ;
  • 오휘빈 (국립암센터 의공학연구과) ;
  • 황정민 (서울대학교 의과대학 안과학교실)
  • Published : 2009.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

Fundus images are highly useful in evaluating patients' retinal conditions in diagnosing eye diseases. In particular, vessel regions are essential in diagnosing diabetes and hypertension. In this paper, we used top-hat filter to compensate for non-uniform background. Image contrast was enhanced by using contrast limited adaptive histogram equalization (CLAHE) method. Hessian matrix was next applied to detect vessel regions. Results indicate that our method is 1.3% more accurate than matched filter method. Our proposed method is expected to contribute to diagnosing eye diseases.

망막 질환의 진단에서 안저영상은 환자의 망막 상태에 대한 객관적인 평가와 기록에 중요하다. 특히 혈관의 분석은 당뇨병, 고혈압 등의 진단과 경과 관찰에 매우 중요하다. 혈관 영역을 검출하기 위해 톱-햇(Top-hat) 필터를 사용하여 균일하지 않은 배경 영상을 보상하고, 대비 제한의 적응적 히스토그램 보정(contrast limited adaptive histogram equalization) 방법을 적용하여 대비를 향상시켰다. 영상에 전처리를 한 후 헤이지안 행렬(hessian matrix)을 적용하여 혈관 성분을 검출한 결과 제안된 방법이 기존의 정합 필터(matched filter) 방법보다 약 1.3% 더 정확하였다. 결론으로 제안한 알고리즘은 안저 영상에서 혈관 영역을 검출하는데 있어서 기존 방법에 비해서 향상되었다.

Keywords

References

  1. Yen KG, Hess D, Bruke B, Johnson RA, Feuer WJ and Flynn JT. "Telephoto screening to detect retinopathy of prematurity : preliminary study of the optimum time to employ digital fundus camera imaging to detect ROP," J AAPOS, Vol.6, No.2, pp. 64-70, 2002. https://doi.org/10.1067/mpa.2002.121616
  2. 이병일 and 최홍국 "의료영상처리 및 분석 방법," 한국멀티미디어학회지, Vol.4, No.4, pp. 51-69, 2000.
  3. Lim JI, LaBree L, Nichols T and Cardenas I. "A comparison of digital nonmydriatic fundus imaging with standard 35-millimeter slides for diabetic retinopathy," Ophthalmology, Vol.107, No.5, pp. 866-870, 2000. https://doi.org/10.1016/S0161-6420(00)00057-9
  4. T. N. Pappas and J. S. Lim, "A new method for estimation of coronary artery dimensions in angiograms," IEEE Trans. Acoust. Speech, Signal Processing, Vol.36, pp.1501-1512, Sept. 1988. https://doi.org/10.1109/29.90378
  5. M. A. T. Figuereido and J. M. N. Leitao, "A nonsmoothing approach to the estimation of vessel contours in angiograms," IEEE Trans. Med. Imag., Vol.14, pp. 162-172, Mar. 1995. https://doi.org/10.1109/42.370413
  6. Y. A. Tolias and S. M. Panas, "A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering," IEEE Trans. Med. Imag., Vol.17, No.2, pp. 263-273, Apr. 1998. https://doi.org/10.1109/42.700738
  7. Hoover, A., Kouznetsova, V., and Goldbaum, M., "Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response," IEEE Trans. Med. Imag., Vol.19, No.3, pp. 203-210, Apr. 1998.
  8. A. Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels," IEEE Trans. Med. Imag., Vol. 22, No.8, pp. 951-958, Aug. 2003. https://doi.org/10.1109/TMI.2003.815900
  9. M. Sofka and C. V. Stewart, "Retinal vessel centerline extraction using multi scale matched filters, confidence and edge measures," IEEE Trans. Med. Imag., Vol.25, No.12, pp. 1531-1546, Dec. 2006. https://doi.org/10.1109/TMI.2006.884190
  10. M. Sofka and C. V. Stewart, "Erratum to retinal vessel centerline extraction using multi-scale matched filters, confidence and edge measures," IEEE Trans. Med. Imag., Vol.26, No.1, pp. 133-133, Jan. 2007. https://doi.org/10.1109/TMI.2006.889977
  11. B. M. Ege, O. K. Hejlesen, O. V. Larsen and Bek T, "The relationship between age and colour content in fundus images," Acta Ophthalmologica Scandinavica, Vol.80, No.5, pp. 485-489, 2002. https://doi.org/10.1034/j.1600-0420.2002.800505.x
  12. 강전권, 한영환 홍승홍, 이응혁 and 민흥기, "안저영상 해석을 위한 특징영역의 분할에 관한 연구," 대한의용생체공학회 의공학회지, Vol.16, No.2, pp. 121-128, 1995.
  13. Gonzalez RC and W.R., Digital image processing using MatLab. 2004: ITC.Seoul.
  14. Zuiderveld, K., "Contrast limited adaptive histogram equalization," Graphics gems IV, Academic Press Professional, Inc., San Diego, CA,.
  15. Otsu, N., "A threshold selection method from gray_level histograms," IEEE Trans Systems Man and Cybernetics, Vol.9, No.1, pp. 62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  16. M. Niemeijer and B. van Ginneken, 2002 [Onlinel.Avaliable.http://www.isi.uu.nl/research/Databases/DRIVE/results/php
  17. Sato, Y., Shin Nakajima, Nobuyuki Shiragaa, Hideki Atsumia, Shigeyuki Yoshidab, Thomas Kollerc, Guido Gerigc and Ron Kikinisa, "Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images," Med. Image Anal., Vol.2, No.2, pp. 143-168, 1998. https://doi.org/10.1016/S1361-8415(98)80009-1
  18. Y. Sato, C.-F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura and R. Kikinis, "Tissue classification based on 3D local intensity structures for volume rendering," IEEE Trans Visualization and Computer Graphics, Vol.6, No.2, pp. 160-180, 2000. https://doi.org/10.1109/2945.856997
  19. Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig, S. Yoshida, and R. Kikinis. "3d multi-scale line filter for segmentation and visualization of curvilinear structures in medical images," CVR Med-MRCAS"97, pp. 213-222, Mar. 1997. Lecture Notes in Computer Science 1205, Springer Verlag.