An image processing approach for rigid gas-permeable lens base-curve identification

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.