Photoreceptor cell counting in adaptive optics retinal images using content-adaptive filtering

Automated counting of photoreceptor cells in high-resolution retinal images generated by adaptive optics (AO) imaging systems is important due to its potential for screening and diagnosis of diseases that affect human vision. A drawback in recently reported photoreceptor cell counting methods is that they require user input of cell structure parameters. This paper introduces a method that overcomes this shortcoming by using content-adaptive filtering (CAF). In this method, image frequency content is initially analyzed to design a customized filter with a passband to emphasize cell structures suitable for subsequent processing. The McClellan transform is used to design a bandpass filter with a circularly symmetric frequency response since retinal cells have no preferred orientation. The automated filter design eliminates the need for manual determination of cell structure parameters, such as cell spacing. Following the preprocessing step, cell counting is performed on the binarized filtered image by finding regional points of high intensity. Photoreceptor cell count estimates using this automated procedure were found to be comparable to manual counts (gold standard). The new counting method when applied to test images showed overall improved performance compared with previously reported methods requiring user-supplied input. The performance of the method was also examined with retinal images with variable cell spacing.