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首页> 外文期刊>Knowledge-Based Systems >Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion
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Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion

机译：通过模糊逻辑，ANN，SVM和分类器融合对视网膜血管分割中描述性统计特征进行性能分析

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Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness in the world. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of blood vessels of the retina. In this work, the performance of descriptive statistical features in retinal vessel segmentation is evaluated by using fuzzy logic, an artificial neural network classifier (ANN), a support vector machine (SVM), and classifier fusion. Newly constructed eight features are formed by statistical moments. Mean and median measurements of image pixels' intensity values in four directions, horizontal, vertical, up-diagonal, and down-diagonal, are calculated. Features, F1, F2, F3, and F4 are calculated as the mean values and F5, F6, F7, and F8 are calculated as the median values of a processed pixel in each direction. A fuzzy rule-based classifier, an ANN, a SVM, and a classifier fusion are designed. The publicly available DRIVE and STARE databases are used for evaluation. The fuzzy classifier achieved 93.82% of an overall accuracy, 72.28% of sensitivity, and 97.04% of specificity. For the ANN classifier, 94.2% of overall accuracy, 67.7% of sensitivity, and 98.1% of specificity are achieved on the DRIVE database. For the STARE database, the fuzzy classifier achieved 92.4% of overall accuracy, 75% of sensitivity, and 94.3% of specificity. The ANN classifier achieved the overall accuracy, sensitivity, and specificity as 94.2%, 56.9%, and 98.4%, respectively. Although the overall accuracy of the SVM is calculated lower than the fuzzy and the ANN classifiers, it achieved higher sensitivity rates. Designed classifier fusion achieved the best performance among all by using the proposed statistical features. Its overall accuracy, sensitivity, and specificity are calculated as 95.10%, 74.09%, 98.35% for the DRIVE and 95.53%, 70.14%, 98.46 for the STARE database, respectively. The experimental results validate that the descriptive statistical features can be employed in retinal vessel segmentation and can be used in rule-based and supervised classifiers. (C) 2016 Elsevier B.V. All rights reserved.
机译：糖尿病性视网膜病是最常见的糖尿病眼病，也是世界上失明的主要原因。糖尿病视网膜病变的早期诊断可以通过视网膜血管的分割来完成。在这项工作中，通过使用模糊逻辑，人工神经网络分类器（ANN），支持向量机（SVM）和分类器融合来评估描述性统计特征在视网膜血管分割中的性能。统计矩形成了新构造的八个特征。计算图像像素在四个方向（水平，垂直，上对角线和下对角线）的强度值的平均值和中值。将特征F1，F2，F3和F4计算为平均值，将F5，F6，F7和F8计算为每个方向上已处理像素的中值。设计了基于模糊规则的分类器，人工神经网络，支持向量机和分类器融合。公开可用的DRIVE和STARE数据库用于评估。模糊分类器达到了93.82％的总体准确度，72.28％的灵敏度和97.04％的特异性。对于ANN分类器，在DRIVE数据库上实现了94.2％的总体准确度，67.7％的灵敏度和98.1％的特异性。对于STARE数据库，模糊分类器实现了92.4％的总体准确度，75％的灵敏度和94.3％的特异性。 ANN分类器的总体准确性，敏感性和特异性分别为94.2％，56.9％和98.4％。尽管计算得出的SVM总体准确性低于模糊分类器和ANN分类器，但它实现了更高的灵敏度。通过使用建议的统计特征，设计的分类器融合实现了最佳的性能。对于DRIVE和STARE数据库，其总体准确性，敏感性和特异性分别计算为95.10％，74.09％，98.35％和95.53％，70.14％，98.46。实验结果证实，描述性统计特征可用于视网膜血管分割，并可用于基于规则和监督的分类器。 （C）2016 Elsevier B.V.保留所有权利。

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