Background and Objectives: Retinal blood vessels classification into arterioles and venules is a major task for biomarker identification. Especially, clustering of retinal blood vessels is a challenging task due to factors affecting the images such as contrast variability, non-uniform illumination etc. Hence, a high performance automatic retinal vessel classification system is highly prized. In this paper, we propose a novel unsupervised methodology to classify retinal vessels extracted from fundus camera images into arterioles and venules.

Methods: The proposed method utilises the homomorphic filtering (HF) to preprocess the input image for non-uniform illumination and denoising. In the next step, an unsupervised multiscale line operator segmentation technique is used to segment the retinal vasculature before extracting the discriminating features. Finally, the Locally Consistent Gaussian Mixture Model (LCGMM) is utilised for unsupervised sorting of retinal vessels.

Results: The performance of the proposed unsupervised method was assessed using three publicly accessible databases: INSPIRE-AVR, VICAVR, and MESSIDOR. The proposed framework achieved $90.14\%,$ $90.3\%$ and $93.8\%$ classification rate in zone B for the three datasets respectively.

Conclusions: The proposed clustering framework provided high classification rate as compared to conventional Gaussian mixture model using Expectation-Maximisation (GMM-EM) approach, thus have a great capability to enhance computer assisted diagnosis and research in field of biomarker discovery.

背景与目的：将视网膜血管分为小动脉和小静脉是生物标志物鉴定的主要任务。尤其是，由于影响图像的因素，例如对比度变化，照明不均匀等，视网膜血管的聚类是一项艰巨的任务。因此，高性能的视网膜血管自动分类系统受到高度重视。在本文中，我们提出了一种新颖的无监督方法，可以将从眼底照相机图像中提取的视网膜血管分类为小动脉和小静脉。

方法：所提出的方法利用同态滤波（HF）对输入图像进行预处理，以实现不均匀的照明和去噪。下一步，在提取区分特征之前，使用无监督的多尺度线算子分割技术对视网膜脉管系统进行分割。最后，局部一致的高斯混合模型（LCGMM）用于无​​监督的视网膜血管分类。

结果：使用三个可公开访问的数据库（INSPIRE-AVR，VICAVR和MESSIDOR）评估了所提出的无监督方法的性能。拟议框架得以实现$90.14％，$ $90.3％$ 和 $93.8％$ B区中三个数据集的分类率。

结论：与使用期望最大化（GMM-EM）方法的传统高斯混合模型相比，该聚类框架提供了较高的分类率，因此具有增强生物标记物发现领域的计算机辅助诊断和研究的强大能力。