The structure of blood vessels play a crucial role in diagnoses of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The correct segmentation of retinal blood vessels is a crucial step in the study of retinal fundus images. We proposed a simple unsupervised approach by using a combination of Hessian based approach and intensity transformation approach. We have applied CLAHE for enhancing the contrast of the retinal fundus images. An enhanced version of PSO algorithm is applied for contextual region tuning of CLAHE. Morphological filter and Wiener filter are used to de-noise the enhanced image. The eigenvalues are obtained from the Hessian matrix at two different scales to extract thick and thin vessel enhanced images separately. The intensity transformation approach is separately applied to the enhanced image to maximize the vessel details. Global Otsu thresholding is applied on intensity transformed image and thick vessel enhanced image whereas ISODATA local thresholding is applied on thin vessel enhanced image. Finally, a simple post-processing step based on the region parameters such as area, eccentricity, and solidity is used. The region parameters are obtained for each connected component in input binary images. The threshold values of region parameters are empirically investigated and applied to each of the three binary images to remove the non-vessel components. The thresholded images are combined by applying logical OR operator, which resulted in the final segmented binary image. We assessed our developed framework on the open-access CHASE_DB1 and DRIVE datasets, achieving a sensitivity of 0.7776 and 0.7851, and an accuracy of 0.9505 and 0.9559 respectively. These results outperform several state-of-the-art unsupervised methods. The reduced computational complexity and significantly improved evaluation metrics advocates for its use in the automated diagnostic systems for retinal image analysis.

中文翻译：

---

使用 Hessian 和基于强度的方法进行无监督视网膜血管分割


血管结构在青光眼和糖尿病视网膜病变（DR）等各种威胁视力的疾病的诊断中发挥着至关重要的作用。视网膜血管的正确分割是视网膜眼底图像研究的关键一步。我们结合使用基于 Hessian 的方法和强度变换方法，提出了一种简单的无监督方法。我们应用 CLAHE 来增强视网膜眼底图像的对比度。 PSO 算法的增强版本应用于 CLAHE 的上下文区域调整。使用形态滤波器和维纳滤波器对增强图像进行去噪。从两个不同尺度的Hessian矩阵获得特征值，以分别提取粗血管和细血管增强图像。强度变换方法单独应用于增强图像以最大化血管细节。全局 Otsu 阈值应用于强度变换图像和厚血管增强图像，而 ISODATA 局部阈值应用于薄血管增强图像。最后，使用基于区域参数（例如面积、偏心率和坚固性）的简单后处理步骤。获得输入二值图像中每个连接分量的区域参数。根据经验研究区域参数的阈值并将其应用于三个二值图像中的每一个以去除非血管成分。通过应用逻辑或运算符组合阈值图像，从而产生最终的分割二值图像。我们在开放访问的 CHASE_DB1 和 DRIVE 数据集上评估了我们开发的框架，分别达到了 0.7776 和 0.7851 的灵敏度以及 0.9505 和 0.9559 的准确度。 这些结果优于几种最先进的无监督方法。降低的计算复杂性和显着改进的评估指标提倡将其用于视网膜图像分析的自动诊断系统。