Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia.
Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, 576104, India.
Comput Biol Med. 2017 Sep 1;88:72-83. doi: 10.1016/j.compbiomed.2017.06.022. Epub 2017 Jun 29.
Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.
青光眼是一种视神经病变，其特征为视神经损伤和伴随的视野缺损。早期诊断和治疗对于防止不可逆转的视力丧失和最终失明至关重要。目前，用于分析视神经和视网膜神经纤维层（RNFL）的计算机辅助技术价格昂贵，需要经过训练的专家进行敏锐的解读。因此，对于成本效益高且准确的青光眼诊断，自动化系统是非常需要的。本文提出了一种新的方法和计算机诊断系统。自适应直方图均衡化用于将彩色图像转换为灰度图像，然后对这些图像进行 Leung-Malik（LM）、Schmid（S）和最大响应（MR4 和 MR8）滤波器组的卷积。典型图像中的基本微观结构称为纹理。卷积过程产生纹理。从这些纹理中提取局部配置模式（LCP）特征。使用顺序浮动向前搜索（SFFS）方法选择显著特征，并使用统计 t 检验对其进行排序。最后，使用各种分类器将图像分类为正常和青光眼类别。使用来自 LM 滤波器组的六个特征和 k-最近邻（kNN）分类器，实现了 95.8%的高分类准确率。还制定了青光眼综合指数（GRI）以获得可靠有效的系统。
