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.

中文翻译：

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一种使用超声图像自动检测视网膜脱离的新型混合方法。

视网膜脱离（RD）是一种眼部紧急情况，需要快速干预以防止永久性视力丧失。通常，眼科医师使用眼超声波来增强他们在检测具有中度混浊的眼睛的RD时的判断力，从而无法进行视网膜评估。但是，由于存在噪音，超声（US）图像的质量可能会降低，并且其他视网膜状况可能会导致膜性回声。所有这些都会影响诊断的准确性。因此，为了克服上述问题，我们提出了一种自动化系统，该系统使用texton，高阶谱（HOS）累积量和局部敏感判别分析（LSDA）技术来检测RD。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RD进行分类，最高精度为99.13％。