Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Enhancement of a Choroid Vessel Using Conditional Erosion in ICGA Image

형광안저 조영영상에서 선택적 영역침식을 이용한 맥락막혈관영상 향상

  • 정지운 (경북대학교 대학원 의용생체공학과) ;
  • 김필운 (경북대학교 대학원 의용생체공학과) ;
  • 이윤정 (경북대학교 대학원 의용생체공학과) ;
  • 김명남 (경북대학교 의학전문대학원 의공학교실)
  • Published : 2009.08.30
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Abstract

In this paper, we proposed new method to enhance choroidal vessels by suppressing retina vessels brightness. It is well-known that CNV(choroidal neovascularization) is related with sight loss. The main feature of CNV is the occurrence of new vessels in choroid. Unfortunately, because retina vessels brightness is stronger than choroidal vessels brightness in ICGA(indocynanine green angiography) image, so that the choroidal vessels were hardly recognized. Therefore, for correct diagnosis, the choroidal vessels must be enhanced in ICGA image. The proposed enhancement method consists of 3 strategies. First, the retina vessels were detected by multi scale enhancement technique, hysteresis thresholding, KNN(Kth-nearest neighbor) classification method. And then, a retina vessel mask was generated from detection result. Next, the brightness of retina vessels was suppressed by the proposed conditional region erosion method and mask region until the mask region was vanished. Finally, the brightness of choroidal vessel was enhanced on processed image. Through an experiment, we had confirmed that the proposed method was robust and efficient.

본 논문에서는 망막혈관의 밝기를 저하시킴으로써 맥락막혈관을 향상시킬 수 있는 새로운 방법을 제안하였다. 맥락막혈관신생 현상은 실명과 관련이 있는 것으로 알려져 있으며 이 현상의 주요 특징은 맥락막에 새로운 혈관의 발생이다. 인도시아닌 형광안저 조영영상에서 맥락막혈관의 밝기보다 망막혈관의 밝기가 더 크기 때문에 맥락막혈관을 관찰하기가 어렵다. 따라서 정화한 진단을 위해서는 영상에서 맥락막혈관의 밝기가 향상될 필요가 있다. 제안한 향상 방법은 3단계에 걸쳐 수행된다. 먼저, 다중크기 향상기법, 히스테리시스 문턱화, KNN 분류법을 이용하여 망막혈관을 검출하고 이로 부터 망막혈관 마스크영상을 만든다. 그런 다음, 마스크영역이 없애질 때까지 마스크영역과 제안한 선택적 영역침식방식을 이용하여 망막혈관의 밝기를 저하시킨다. 최종적으로 처리된 영상에 대하여 맥락막혈관의 밝기를 향상시켰다. 실험을 통하여 제안한 방법이 안정적이고 효과적임을 확인하였다.

Keywords

References

  1. Y. Hong, S. Makita, M. Yamanari, M. Miura, S. Kim, T. Yatagai, and Y. Yasuno, "Three-Dimensional Visualization of Choroidal Vessels by Using Standard and Ultra-High Resolution Scattering Optical Coherence Angiography," Optics Express, Vol.15, Issue12, pp. 7538-7550, 2007. https://doi.org/10.1364/OE.15.007538
  2. 정흠, 강세웅, 이원기, 망막 질환과 형광안저혈관조영, 내외학술, 서울, 2000.
  3. K. Akita and H. Kuga, "A Computer Method of Understanding Ocular Fundus Images," Pattern Recognition, Vol.15, Issue6, pp. 431-443, 1982. https://doi.org/10.1016/0031-3203(82)90022-X
  4. D.C. Wu, B. Schwartz, J. Schwoerer, and R. Banwatt, "Retinal Blood Vessel Width Measured on Color Fundus Photographs by Image Analysis," Acta Ophthalmol. Scand. Suppl., Vol.215, pp. 33-40, 1995.
  5. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, "Detection of Blood Vessels in Retinal Images Using Two-Dimensional Matched Filters," IEEE Trans. Medical. Imaging, Vol.8, No.3, pp. 263-269, 1989. https://doi.org/10.1109/42.34715
  6. A. Hoover, V. Kouznetsova, and M. Goldbaum, "Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response," IEEE Trans. Medical Imaging, Vol.19, No.3, pp. 203-210, 2000. https://doi.org/10.1109/42.845178
  7. L. Zhou, M. S. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, "The Detection and Quantification of Retinopathy Using Digital Angiograms," IEEE Trans. Medical Imaging, Vol.13, No.3, pp. 619-626, 1994. https://doi.org/10.1109/42.363106
  8. X. Gao, A. A. Bharath, A. J. Hughes, A. Stanton, N. Chapman, and S. Thom, "Towards Retinal Vessel Parametrisation," Proc. of SPIE Medical Imaging, Vol.3034, pp. 734-744, 1997.
  9. M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, A. A. Bharath, and K. H. Parker, "Segmentation of Blood Vessels From Red-Free and Fluorescein Retina Images," Medical Image Analysis, Vol.11, Issue1, pp. 47-61, 2007. https://doi.org/10.1016/j.media.2006.11.004
  10. A. P. Condurache and T. Aach, "Vessels Segmentation in Angiograms Using Hysteresis Thresholding," Proc. of IAPR Conference on Machine Vision Applications, 2005.
  11. B. Zang and S. N. Srihari, "A Fast Algorithm for Finding K-Nearest Neighbors with Non-Metric Dissimilarity," Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition, pp. 13-18, 2002.
  12. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, "Multiscale Vessel Enhancement Filtering," Lecture Notes in Computer Science, Vol.1496, pp. 130-137, 1998.
  13. C. Lorenz, I. C. Carlsen, T. M. Buzug, C. Fassnacht, and J. Weese, "Multiscale Line Segmentation with Automatic Estimation of Width, Contrast and Tangential Direction in 2D and 3D Medical Images," Lecture Notes in Computer Science, Vol.1205, pp. 233-242, 1997.
  14. 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," Lecture Notes in Computer Science, Vol.1205, pp. 213-222, 1997.
  15. D. Eberly, Ridges in Image and Data Analysis : Computational Imaging and Vision, Kluwer Academic Publishers, Netherlands, 1996.
  16. R.C. Gonzales, Digital Image Procesing, Prentice Hall, 2002.