Koh Joel En Wei, Raghavendra U, Gudigar Anjan, Ping Ooi Chui, Molinari Filippo, Mishra Samarth, Mathavan Sinnakaruppan, Raman Rajiv, Acharya U Rajendra
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore.
Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
Comput Biol Med. 2020 May;120:103704. doi: 10.1016/j.compbiomed.2020.103704. Epub 2020 Mar 19.
Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.
视网膜脱离（RD）是一种眼部急症，需要迅速干预以防止永久性视力丧失。一般来说，眼科医生会使用眼部超声来增强他们对患有妨碍视网膜评估的介质混浊的眼睛中视网膜脱离的检测判断。然而，由于噪声的存在，超声（US）图像的质量可能会下降，并且其他视网膜状况可能会导致膜状回声。所有这些都会影响诊断的准确性。因此，为了克服上述问题，我们提出了一种使用纹理、高阶谱（HOS）累积量和局部敏感判别分析（LSDA）技术来检测视网膜脱离的自动化系统。我们开发的方法能够使用支持向量机分类器对玻璃体后脱离和视网膜脱离进行分类，最高准确率为99.13%。我们的系统准备好使用更多样化的超声图像进行测试，并帮助眼科医生做出更准确的诊断。
