This research is aimed at accurate identification of base-curve in rigid gas-permeable (RGP) lens based on supervised image processing and classification of Pentacam four refractive maps in irregular astigmatism cases. Base-curve, is typically identified based on expert’s opinion of the corneal structure of the eye. Studies have applied time-consuming methods, focusing on manual and device-based techniques. For the identification of the base-curve of a lens, image analysis is proposed. As each map in the four refractive maps is of a singular view, multi-view learning is recommended to provide a single representation. To this end, an authentic dataset consisting of 247 labeled Pentacam four refractive maps was gathered in which labels were verified manually. We have proposed two novel feature extraction techniques in this domain: quantization-based radial–sectoral segmentation (QRSS) in image processing and deep convolutional neural networks. Feature fusion is applied and RGP base-curve is identified by the regression layer of a neural network. A combination of QRSS and multilayered perceptron delineates the best result, achieving a coefficient of determination of 0.9642 and satisfactory mean square error (0.0089) which is acceptable by the experts. The proposed multi-view model could improve base-curve detection accuracy, with less trial and error and patient visits in the lens fitting process.

中文翻译：

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一种刚性透气镜片基弧识别的图像处理方法

本研究旨在基于不规则散光情况下的 Pentacam 四折射图的监督图像处理和分类，准确识别刚性透气 (RGP) 镜片中的基曲线。基曲线通常是根据专家对眼睛角膜结构的意见来确定的。研究采用了耗时的方法，侧重于手动和基于设备的技术。为了识别镜片的基弧，提出了图像分析。由于四个折射图中的每个图都是单一视图，因此建议使用多视图学习来提供单一表示。为此，收集了由 247 个标记的 Pentacam 四折射图组成的真实数据集，其中手动验证了标签。我们在该领域提出了两种新颖的特征提取技术：图像处理和深度卷积神经网络中基于量化的径向扇区分割（QRSS）。应用特征融合，并通过神经网络的回归层识别 RGP 基曲线。QRSS 和多层感知器的组合描绘了最好的结果，达到了 0.9642 的确定系数和令人满意的均方误差 (0.0089)，这是专家们可以接受的。所提出的多视图模型可以提高基曲线检测精度，减少镜片装配过程中的反复试验和患者访问。达到确定系数为 0.9642 和令人满意的均方误差 (0.0089)，这是专家可接受的。所提出的多视图模型可以提高基曲线检测精度，减少镜片装配过程中的反复试验和患者访问。达到确定系数为 0.9642 和令人满意的均方误差 (0.0089)，这是专家可接受的。所提出的多视图模型可以提高基曲线检测精度，减少镜片装配过程中的反复试验和患者访问。