Improving the accuracy of structured light calibration methods has led to the development of pixel-wise calibration models built on top of conventional pinhole-camera models. Because phase encodes depth and transversal information, the pixel-wise methods provide high flexibility to map phase to XYZ coordinates. However, there are different approaches for producing phase-to-coordinate mapping, and there is no consensus on the most appropriate one. In this study, we highlight the current limitations, especially in depth range and accuracy, of several recent pixel-wise calibration methods, along with experimental performance verifications. The results show that there are opportunities for further improving these methods to overcome existing limitations from conventional calibration methods, particularly for low-cost hardware
Corneal endothelium assessment is carried out via specular microscopy imaging. However, automated image analysis often fails due to inadequate image quality conditions or the presence of dark regions in pathologies such as Fuchs’ dystrophy. Therefore, an early reliable image classification strategy is required before automated evaluation based on cell segmentation. Moreover, conventional classification approaches rely on manually labeled data which are difficult to obtain. We propose a two-stage semi-supervised classification algorithm, feature detection and prediction of a blurring level and guttae severity that allows us to cluster images based on the degree of segmentation complexity. For validation, we developed a web-based annotation application and surveyed a pair of expert ophthalmologists for grading a portion of the 1169 images. Preliminary results show that this approach provides a reliable and fast approach for corneal endothelial cell (CEC) image classification.
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