Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada N2L3G1.
Comput Biol Med. 2010 Apr;40(4):438-45. doi: 10.1016/j.compbiomed.2010.02.008. Epub 2010 Mar 3.
Accurate extraction of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. The matched filter (MF) is a simple yet effective method for vessel extraction. However, a MF will respond not only to vessels but also to non-vessel edges. This will lead to frequent false vessel detection. In this paper we propose a novel extension of the MF approach, namely the MF-FDOG, to detect retinal blood vessels. The proposed MF-FDOG is composed of the original MF, which is a zero-mean Gaussian function, and the first-order derivative of Gaussian (FDOG). The vessels are detected by thresholding the retinal image's response to the MF, while the threshold is adjusted by the image's response to the FDOG. The proposed MF-FDOG method is very simple; however, it reduces significantly the false detections produced by the original MF and detects many fine vessels that are missed by the MF. It achieves competitive vessel detection results as compared with those state-of-the-art schemes but with much lower complexity. In addition, it performs well at extracting vessels from pathological retinal images.
准确提取视网膜血管是视网膜病变计算机辅助诊断中的一项重要任务。匹配滤波器 (MF) 是一种简单而有效的血管提取方法。然而，MF 不仅会对血管产生响应，还会对非血管边缘产生响应。这将导致频繁的假血管检测。在本文中，我们提出了一种 MF 方法的新扩展，即 MF-FDOG，用于检测视网膜血管。所提出的 MF-FDOG 由原始 MF 组成，原始 MF 是一个零均值高斯函数，以及高斯函数的一阶导数 (FDOG)。通过对视网膜图像对 MF 的响应进行阈值处理来检测血管，而阈值由图像对 FDOG 的响应进行调整。所提出的 MF-FDOG 方法非常简单；然而，它大大减少了原始 MF 产生的假检测，并检测到许多原始 MF 错过的精细血管。与最先进的方案相比，它在血管检测方面取得了竞争结果，但复杂度要低得多。此外，它在从病理性视网膜图像中提取血管方面表现良好。
