Rajashekar Deepthi, Srinivasa Gowri, Vinekar Anand
PES Center for Pattern Recognition, PESIT Bangalore South Campus, Bengaluru, Karnataka, India.
Department Of Computer Science and Engineering, PESIT Bangalore South Campus, Bengaluru, Karnataka, India.
PLoS One. 2016 Oct 6;11(10):e0163923. doi: 10.1371/journal.pone.0163923. eCollection 2016.
Computer aided analysis plays a nontrivial role in assisting the diagnosis of various eye pathologies. In this paper, we propose a framework to help diagnose the presence of Aggressive Posterior Retinopathy Of Prematurity (APROP), a pathology that is characterised by rapid onset and increased tortuosity of blood vessels close to the optic disc (OD). We quantify vessel characteristics that are of clinical relevance to APROP such as tortuosity and the extent of branching i.e., vessel segment count in the defined diagnostic region. We have adapted three vessel segmentation techniques: matched filter response, scale space theory and morphology with local entropy based thresholding. The proposed feature set equips us to build a linear discriminant classifier to discriminate APROP images from clinically healthy images. We have studied 36 images from 21 APROP subjects against a control group of 15 clinically healthy age matched infants. All subjects are age matched ranging from 33-40 weeks of post menstrual age. Experimental results show that we attain 100% recall and 95.45% precision, when the vessel network obtained from morphology is used for feature extraction.
计算机辅助分析在协助诊断各种眼部疾病中发挥着重要作用。在本文中,我们提出了一个框架,以帮助诊断侵袭性早产儿视网膜病变(APROP),这种病变的特征是发病迅速且靠近视盘(OD)的血管迂曲增加。我们量化了与APROP临床相关的血管特征,如迂曲度和分支程度,即在定义的诊断区域内的血管段数量。我们采用了三种血管分割技术:匹配滤波器响应、尺度空间理论和基于局部熵阈值的形态学方法。所提出的特征集使我们能够构建一个线性判别分类器,以区分APROP图像和临床健康图像。我们研究了来自21名APROP受试者的36张图像,并与15名年龄匹配的临床健康婴儿的对照组进行对比。所有受试者年龄匹配,月经后年龄在33 - 40周之间。实验结果表明,当使用从形态学方法获得的血管网络进行特征提取时,我们实现了100%的召回率和95.45%的精确率。