目的 在眼底图像分析中，准确的黄斑中心定位对于糖尿病性视网膜病变的计算机辅助诊断系统具有重要的意义。然而，由于光照不均匀、计算量大及病变的干扰给黄斑中心定位带来了巨大的挑战。因此，为了实现更为准确且高效的黄斑中心检测，提出一种基于血管投影和数学形态学的黄斑中心检测方法。方法 首先，基于数学形态学，提出一种自动的血管检测方法。其次，利用视盘区域的血管分布实现视盘中心的自动定位。再次，根据视盘和黄斑的解剖学结构先验信息，提取感兴趣区域。最后，在感兴趣区域内，通过数学形态学和特征提取定位黄斑中心。结果 本文提出的方法在两个标准的糖尿病视网膜病变数据库DIARETDB0和DIARETDB1上分别取得了96.92%和96.63%的成功率，且总成功率达到96.35%。此外，平均的执行时间分别为8.236 s和8.912 s。结论 实验结果表明，本文方法能快速和准确地定位黄斑中心且其性能明显地优于现有的黄斑中心检测方法。

Objective The macula in the retinal fundus is an oval-shaped pigmented area near the center of the retina and is responsible for detailed central vision, which is needed in activities such as reading and driving. The fovea is located at the center of the macula, which is the site of the sharpest center vision. Any abnormal changes in the macula may cause vision loss. The distance between the lesions and fovea has clinical relevance to the extent of damage. Therefore, accurate localization of the fovea has an important significance in computer-aided diagnosis of diabetic retinopathy. However, fovea detection faces challenges that vary illumination, computational load, and abnormal images. This study develops a novel fovea detection approach by using blood vessels projection and mathematical morphology to achieve accurate and effective fovea detection. A series of techniques has been proposed for fovea detection using color retinal images, which can be divided into anatomical-based and visual-based features. The former criterion involves techniques to determine the region of interest (ROI) that contains fovea by using the geometrical relationship between the fovea and the optic disc diameter. The fovea can be located at a distance that is approximately two and a half disc diameters from the optic disc center. The latter criterion includes techniques of searching the fovea together with visual features of the fovea, such as a round-shaped dark area in the neighborhood of the optic disc. Method Our proposed fovea detection approach consists of four steps. First, we use a series of mathematical morphology techniques that contain open, erosion, and reconstruction operations for automatic blood vessel detection. The center of the optic disc is then determined using the primary distribution of blood vessels within the optic disc (vertical vessel is aggregated, and few blood vessels are present above or below the optic disc). The anatomical prior information between the optic disc and the macula indicates that the fovea can be located approximately two and a half disc diameters from the optic disc center. Thus, an ROI that contains the fovea is extracted. Finally, we conduct fovea localization on the green channel of the corresponding ROI image that contains the fovea. Brightness adjustment is first applied to improve the contrast of the fovea within the ROI to make the fovea region highly visible. Morphological erosion reconstruction operation is used to remove the lesions, which have a similar color as the fovea. The macula has low luminance. Thus, we invert the reconstructed image and extract the binary image using region maximum and Ostu threshold techniques. Dilatation operation is used to smooth the extracted candidate regions. A series of features of candidate regions is extracted to determine the fovea. Result Two standard diabetic retinopathy databases, DIARETDB0 and DIARETDB1, are used to evaluate the effectiveness of the proposed approach. The proposed approach achieves 96.92% and 96.63% success rates on DIARETDB0 and DIARETDB1, respectively, with a total success rate of 96.35%, thereby outperforming state-of-the-art fovea detection approaches. Experimental results indicate that the locations of the macula fovea detected by using our proposed method are close to the corresponding reference positions marked by specialists. Moreover, the average execution time on DIARETDB0 and DIARETDB1 is 8.236 s and 8.912 s, respectively. Conclusion Experimental results indicate that the proposed approach can locate macula fovea quickly and accurately, and performs better than existing macula fovea detection approaches.