Jorjandi Sahar, Rabbani Hossein, Kafieh Raheleh, Amini Zahra
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4399-4402. doi: 10.1109/EMBC.2017.8037831.
Optical Coherence Tomography (OCT) is known as a non-invasive and high resolution imaging modality in ophthalmology. Effecting noise on the OCT images as well as other reasons cause a random behavior in these images. In this study, we introduce a new statistical model for retinal layers in healthy OCT images. This model, namely asymmetric Normal Laplace (NL), fits well the advent of asymmetry and heavy-tailed in intensity distribution of each layer. Due to the layered structure of retina, a mixture model is addressed. It is proposed to evaluate the fitness criteria called Kull-back Leibler Divergence (KLD) and chi-square test along visual results. The results express the well performance of proposed model in fitness of data except for 6 and 7 layers. Using a complicated model, e.g. a mixture model with two component, seems to be appropriate for these layers. The mentioned process for train images can then be devised for a test image by employing the Expectation Maximization (EM) algorithm to estimate the values of parameters in mixture model.
光学相干断层扫描（OCT）是眼科一种非侵入性的高分辨率成像方式。OCT图像上的噪声影响以及其他因素导致这些图像呈现随机行为。在本研究中，我们为健康OCT图像中的视网膜层引入了一种新的统计模型。该模型，即非对称正态拉普拉斯（NL）模型，很好地拟合了各层强度分布中的不对称性和重尾现象。由于视网膜的分层结构，提出了一种混合模型。建议结合视觉结果评估称为库尔贝克 - 莱布勒散度（KLD）和卡方检验的拟合标准。结果表明，除了第6层和第7层外，所提出的模型在数据拟合方面表现良好。对于这些层，使用复杂模型，例如具有两个分量的混合模型，似乎是合适的。然后，通过采用期望最大化（EM）算法估计混合模型中的参数值，可以为测试图像设计上述针对训练图像的过程。
