Automated Identification of Photoreceptor Cones Using Multi-scale Modelling and Normalized Cross-Correlation

Analysis of the retinal photoreceptor mosaic can provide vital information in the assessment of retinal disease. However, visual analysis of photoreceptor cones can be both difficult and time consuming. The use of image processing techniques to automatically count and analyse these photoreceptor cones would be beneficial. This paper proposes the use of multi-scale modelling and normalized cross- correlation to identify retinal cones in image data obtained from a modified commercially available confocal scanning laser ophthalmoscope (CSLO). The paper also illustrates a process of synthetic data generation to create images similar to those obtained from the CSLO. Comparisons between synthetic and manually labelled images and the automated algorithm are also presented.