Balasubramanian Kishore, Ananthamoorthy N P
1 Dr Mahalingam College of Engineering & Technology, Pollachi, India.
2 Hindusthan College of Engineering & Technology, Coimbatore, India.
Proc Inst Mech Eng H. 2019 May;233(5):506-514. doi: 10.1177/0954411919835856. Epub 2019 Mar 20.
Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus images are initially preprocessed for any noise reduction, image enhancement and contrast adjustment. The two-dimensional Gabor Wavelets and Partition Clustering is employed on the preprocessed image to extract the blood vessels. Finally, the combined hybrid features comprising statistical textural, intensity and vessel morphological features, extracted from the image, are used to detect glaucomatous abnormality through the classifiers. A crisp decision can be taken depending on the classifying rates of the classifiers. Public databases RIM-ONE and high-resolution fundus and local datasets are used for evaluation with threefold cross validation. The evaluation is based on performance metrics through accuracy, sensitivity and specificity. The evaluation of hybrid features obtained an overall accuracy of 97% when tested using classifiers. The support vector machine classifier is able to achieve an accuracy of 93.33% on high-resolution fundus, 93.8% on RIM-ONE dataset and 95.3% on local dataset. For extreme learning machine classifier, the accuracy is 95.1% on high-resolution fundus, 97.8% on RIM-ONE and 96.8% on local dataset. An accuracy of 94.5% on high-resolution fundus 92.5% on RIM-ONE and 94.2% on local dataset is obtained for the random forest classifier. Validation of the experiment results indicate that the hybrid features can be deployed in supervised classifiers to discriminate retinal abnormalities effectively.
视网膜图像分析依赖于计算技术辨别眼睛中各种异常情况的有效性，如糖尿病性视网膜病变、黄斑变性和青光眼。青光眼发病时往往不易察觉，其影响只有在后期才会显现。对这类退行性疾病的诊断需要早期诊断和治疗。在这项工作中，通过极限学习机、支持向量机和随机森林等分类器评估视网膜血管分割中统计特征和纹理特征的性能。眼底图像首先进行预处理，以减少噪声、增强图像和调整对比度。对预处理后的图像采用二维伽柏小波和分区聚类来提取血管。最后，从图像中提取的包括统计纹理、强度和血管形态特征的组合混合特征，通过分类器用于检测青光眼异常。根据分类器的分类率可以做出明确的决策。使用公共数据库RIM-ONE、高分辨率眼底和本地数据集进行评估，并采用三倍交叉验证。评估基于通过准确率、灵敏度和特异性的性能指标。使用分类器测试时，混合特征的评估获得了97%的总体准确率。支持向量机分类器在高分辨率眼底上的准确率为93.33%，在RIM-ONE数据集上为93.8%，在本地数据集上为95.3%。对于极限学习机分类器，在高分辨率眼底上的准确率为95.1%，在RIM-ONE上为97.8%，在本地数据集上为96.8%。随机森林分类器在高分辨率眼底上的准确率为94.5%，在RIM-ONE上为92.5%，在本地数据集上为94.2%。实验结果的验证表明，混合特征可以部署在监督分类器中，以有效辨别视网膜异常。
