Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images.

Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting.

Parametric model for the 3D reconstruction of individual fovea shape from OCT data.

As revealed by optical coherence tomography (OCT), the shape of the fovea may vary greatly among individuals. However, none of the hitherto available mathematical descriptions comprehensively reproduces all individual characteristics such as foveal depth, slope, naso-temporal asymmetry, and others. Here, a novel mathematical approach is presented to obtain a very accurate model of the complete 3D foveal surface of an individual, by utilizing recent developments in OCT. For this purpose, a new formula was developed serving as a simple but very flexible way to represent a given fovea. An extensive description of the used model parameters, as well as, of the complete method of reconstructing a foveal surface from OCT data, is presented. Noteworthy, the formula analytically provides characteristic foveal parameters and thus allows for extensive quantification. The present approach was verified on 432 OCT scans and has proved to be able to capture the whole range of asymmetric foveal shapes with high accuracy (i.e. a mean fit error of 1.40 µm).