Wisaeng Kittipol
Technology and Business Information System Unit, Mahasarakham Business School, Mahasarakham University, Mahasarakham 44150, Thailand.
Diagnostics (Basel). 2023 Jan 17;13(3):342. doi: 10.3390/diagnostics13030342.
Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.
自动血管提取在糖尿病视网膜病变（DR）和其他眼部相关疾病的诊断中至关重要。然而，传统的血管提取方法在处理困难情况时往往准确率较低，例如在处理低强度图像以及患有糖尿病视网膜病变的血管时，要同时提取微血管和大血管。本文提出了一种完整的预处理方法，在将增强后的图像传输到一种新颖的血管提取方法之前，先对原始视网膜图像进行增强，该方法结合了三个提取阶段。第一阶段重点是使用加权核模糊C均值（WKFCM）聚类快速提取视网膜血管，以便从视网膜背景中提取血管特征。第二阶段重点是全尺寸图像的准确性，以实现对大血管和微血管的区域血管特征识别，并尽量减少误提取。此阶段通过一个名为基于膨胀的函数（DBF）的训练模型实现数学膨胀算子。最后，在第三阶段凭经验确定一个最佳参数阈值，以去除二值图像中的非血管特征并改善整体血管提取结果。根据通过DRIVE、STARE和DiaretDB0数据集对该方法的评估，所提出的WKFCM-DBF方法分别实现了98.12%、98.20%和98.16%，98.42%、98.80%和98.51%，以及98.89%、98.10%和98.09%的灵敏度、特异性和准确率表现。
