Gao Wei-Wei, Shen Jian-Xin, Wang Yu-Liang, Liang Chun, Zuo Jing
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Feb;33(2):448-53.
In order to automatically detect hemorrhages in fundus images, and develop an automated diabetic retinopathy screening system, a novel algorithm named locally adaptive region growing based on multi-template matching was established and studied. Firstly, spectral signature of major anatomical structures in fundus was studied, so that the right channel among RGB channels could be selected for different segmentation objects. Secondly, the fundus image was preprocessed by means of HSV brightness correction and contrast limited adaptive histogram equalization (CLAHE). Then, seeds of region growing were founded out by removing optic disc and vessel from the resulting image of normalized cross-correlation (NCC) template matching on the previous preprocessed image with several templates. Finally, locally adaptive region growing segmentation was used to find out the exact contours of hemorrhages, and the automated detection of the lesions was accomplished. The approach was tested on 90 different resolution fundus images with variable color, brightness and quality. Results suggest that the approach could fast and effectively detect hemorrhages in fundus images, and it is stable and robust. As a result, the approach can meet the clinical demands.
为了自动检测眼底图像中的出血情况，并开发一种自动化的糖尿病视网膜病变筛查系统，建立并研究了一种基于多模板匹配的局部自适应区域生长新算法。首先，研究了眼底主要解剖结构的光谱特征，以便为不同的分割对象选择RGB通道中的正确通道。其次，通过HSV亮度校正和对比度受限自适应直方图均衡化（CLAHE）对眼底图像进行预处理。然后，通过在前述预处理图像上使用多个模板进行归一化互相关（NCC）模板匹配，从所得图像中去除视盘和血管，找出区域生长的种子点。最后，使用局部自适应区域生长分割来找出出血的确切轮廓，完成病变的自动检测。该方法在90张具有不同颜色、亮度和质量的不同分辨率眼底图像上进行了测试。结果表明，该方法能够快速有效地检测眼底图像中的出血情况，并且稳定可靠。因此，该方法能够满足临床需求。
