The Color Fundus Image Enhancement Algorithm Based on Retinex Theory
Liu Yuhong1, 2, Yan Hongmei1*
1 School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China 2 Department of Physics, Chengdu Medical College, Chengdu 610050,China
Abstract：The color fundus image is usually suffered from poor brightness, low contrast and local detail loss. This paper analyzed drawbacks of Retinex methods, and proposed a new effective enhancement algorithm based on Retinex theory. First of all, luminance components of original image were extracted. Next, a multi-scale retinex algorithm was used on it. The simplest possible color balance algorithm was adopted to modify the gain/offset correction method.At last, we calibrated the red channel information to restore the luminance information. In order to verify the effectiveness of the method, the proposed method was compared with other enhancement algorithms including multi-scale retinex(MSR), multi-scale retinex with color restoration (MSRCR), histogram equalization(HE), contrast limited adaptive histogram equalization (CLAHE) on the DIARETDB0 fundus image database. Experimental results showed that the proposed method had better effect on the color protection, vascular contrast improvement and enhance image details than the other Retinex algorithms and conventional image enhancement methods. The information entropy was increased by 5% to 7% and the peak signal-to-noise ratio (PSNR) was 1～2 times higher than the conventional methods. The objective image quality index was significantly better thanthe other fundus image enhancement methods. This method is of significance to further fundus image recognition.
刘玉红,颜红梅. 基于Retinex理论的眼底彩色图像增强算法[J]. 中国生物医学工程学报, 2018, 37(3): 257-265.
Liu Yuhong, Yan Hongmei. The Color Fundus Image Enhancement Algorithm Based on Retinex Theory. Chinese Journal of Biomedical Engineering, 2018, 37(3): 257-265.
[1] Fadzil MHA, Nugroho HA, Nugroho H, et al. Contrast enhancement of retinal vasculature in digital fundus image[C]// International Conference on Digital Image Processing. IEEE Computer Society, 2009:137-141. [2] Hani AFM, Nugroho HA. Retinal vasculature enhancement using independent component analysis [J]. Journal of Biomedical Science & Engineering, 2009,2(7):543-549. [3] Marin D, Aquino A, Gegundez-Arias ME, et al. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features [J]. IEEE Transactions on Medical Imaging, 2011,30(1):146-158. [4] Rahim HA, Ibrahim AS, Zaki WMDW, et al. Methods to enhance digital fundus image for diabetic retinopathy detection[C]// International Colloquium on Signal Processing & ITS Applications. IEEE, 2014:221-224. [5] 刘尚平, 陈骥. 荧光视网膜图像的照度均衡及自适应血管增强算法 [J]. 光电子·激光, 2011(5):793-797. [6] Reza AM. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement [J]. Journal of Vlsi Signal Processing Systems for Signal Image & Video Technology, 2004,38(1):35-44. [7] Setiawan AW, Mengko TR, Santoso OS, et al. Color retinal image enhancement using CLAHE[C]// International Conference on ICT for Smart Society. IEEE, 2013:1-3. [8] Bai X, Zhou F, Xue B. Infrared image enhancement through contrast enhancement by using multiscale new top-hat transform [J]. Infrared Physics & Technology, 2011,54(2):61-69. [9] 蒋先刚,熊娟, 丘赟立,等. 基于hessian特征的视网膜血管图像的增强滤波算法 [J]. 华东交通大学学报, 2013(3):37-43. [10] 刘玉红, 谢正祥. 基于视觉感知的视网膜图像增强方法 [J]. 光电子技术, 2016(2):107-110. [11] 段彦华, 杨春兰, 刘冰. 眼底数码图像血管分割预处理关键技术研究 [J]. 北京生物医学工程, 2015,34(4):345-349. [12] 张雪峰, 赵莉. 基于改进retinex的图像增强算法 [J]. 南京理工大学学报(自然科学版), 2016,40(1):24-28. [13] Rahman Z, Jobson DJ, Woodell GA. Multi-scale retinex for color image enhancement[C]//Proceedings of International Conference on Image Processing. IEEE, 2002(3):1003-1006. [14] Jobson DJ, Rahman Z, Woodell GA. A multiscale retinex for bridging the gap between color images and the human observation of scenes [J]. IEEE Transactions on Image Processing, 1997,6(7):965-976. [15] Limare N, Lisani JL, Morel JM, et al. Simplest color balance [J]. Image Processing on Line, 2011,(1):297-315. [16] Land EH, Mccann JJ. Lightness and retinex theory [J]. Journal of the Optical Society of America, 1971,61(1):1-11. [17] Petro AB, Sbert C, Morel JM. Multiscale retinex [J]. Image Processing on Line, 2014,(4):71-88. [18] Kauppi T, Kalesnykiene V, Kamarainen JK, et al. Diaretdb0: Evaluation database and methodology for diabetic retinopathy algorithms [DB].http://www.it.lut.fi/project/imageret/diaretdb1/2007-06-19/2017-10-20.
