Faculty of Informatics, University of Debrecen, POB 12, 4010 Debrecen, Hungary.
Comput Biol Med. 2014 Nov;54:156-71. doi: 10.1016/j.compbiomed.2014.09.001. Epub 2014 Sep 16.
In this paper, we propose a method for the automatic detection of exudates in digital fundus images. Our approach can be divided into three stages: candidate extraction, precise contour segmentation and the labeling of candidates as true or false exudates. For candidate detection, we borrow a grayscale morphology-based method to identify possible regions containing these bright lesions. Then, to extract the precise boundary of the candidates, we introduce a complex active contour-based method. Namely, to increase the accuracy of segmentation, we extract additional possible contours by taking advantage of the diverse behavior of different pre-processing methods. After selecting an appropriate combination of the extracted contours, a region-wise classifier is applied to remove the false exudate candidates. For this task, we consider several region-based features, and extract an appropriate feature subset to train a Naïve-Bayes classifier optimized further by an adaptive boosting technique. Regarding experimental studies, the method was tested on publicly available databases both to measure the accuracy of the segmentation of exudate regions and to recognize their presence at image-level. In a proper quantitative evaluation on publicly available datasets the proposed approach outperformed several state-of-the-art exudate detector algorithms.
在本文中，我们提出了一种用于自动检测数字眼底图像中渗出物的方法。我们的方法可以分为三个阶段：候选物提取、精确轮廓分割和候选物的真假渗出物标记。对于候选物检测，我们借鉴了基于灰度形态学的方法来识别可能包含这些亮病变的区域。然后，为了提取候选物的精确边界，我们引入了一种复杂的基于主动轮廓的方法。也就是说，为了提高分割的准确性，我们利用不同预处理方法的不同行为提取额外的可能轮廓。在选择提取轮廓的合适组合后，应用基于区域的分类器去除假渗出候选物。对于这项任务，我们考虑了几种基于区域的特征，并提取适当的特征子集来训练朴素贝叶斯分类器，进一步通过自适应增强技术进行优化。关于实验研究，该方法在公开可用的数据库上进行了测试，以衡量渗出区域分割的准确性，并识别图像级别的渗出物存在。在对公开可用数据集的适当定量评估中，所提出的方法优于几种最先进的渗出物检测算法。
