Smart Engineering System Research Group (SESRG), Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Selangor, Malaysia.
Smart Engineering System Research Group (SESRG), Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Selangor, Malaysia.
Comput Methods Programs Biomed. 2018 Feb;154:71-78. doi: 10.1016/j.cmpb.2017.10.026. Epub 2017 Oct 31.
Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images.
The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.
The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.
A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.
翼状胬肉是一种眼部疾病，由纤维血管组织侵犯角膜区域引起。如果组织生长到瞳孔区域，可能会导致视力模糊。在这项研究中，我们提出了一种基于眼前部照相图像（也称为前节照相图像）自动区分翼状胬肉和非翼状胬肉（正常）病例的检测方法。
翼状胬肉筛查系统在两个正常眼数据库（UBIRIS 和 MILES）和两个翼状胬肉数据库（澳大利亚翼状胬肉和巴西翼状胬肉）上进行了测试。该系统包括四个模块：（i）使用 HSV-Sigmoid 增强翼状胬肉体组织的预处理模块；（ii）区分角膜区域和翼状胬肉体组织的分割模块；（iii）使用圆度比、哈拉斯圆形度、偏心率和密实度提取角膜特征的特征提取模块；（iv）识别是否存在翼状胬肉的分类模块。使用支持向量机（SVM）和人工神经网络评估系统性能。
在角膜分割模块中引入了三步帧差分技术。输出图像成功覆盖了感兴趣区域，平均准确率为 0.9127。使用 SVM 的拟议系统的性能提供了最有希望的结果，敏感性、特异性和曲线下面积分别为 88.7%、88.3%和 95.6%。
使用所提出的模块成功开发了用于计算机辅助翼状胬肉筛查的基本平台。所提出的系统可以合理地区分翼状胬肉和非翼状胬肉病例。在我们的未来工作中，将开发一个标准分级系统来识别翼状胬肉病例的严重程度。该系统有望提高农村社区对翼状胬肉的认识。
