Pathology segmentation in retinal images of patients with diabetic retinopathy is important to help better understand disease processes. We propose an automated level-set method with Fourier descriptor-based shape priors. A cost function measures the difference between the current and expected output. We applied our method to enface images generated for seven retinal layers and determined correspondence of pathologies between retinal layers. We compared our method to a distance-regularized level set method and show the advantages of using well-defined shape priors. Results obtained allow us to observe pathologies across multiple layers and to obtain metrics that measure the co-localization of pathologies in different layers.
Purpose. The purpose of the study is to report a method for en face imaging of subretinal fluid (SRF) due to age-related macular degeneration (AMD) based on spectral domain optical coherence tomography (SDOCT). Methods. High density SDOCT imaging was performed at two visits in 4 subjects with neovascular AMD and one healthy subject. En face OCT images of a retinal layer anterior to the retinal pigment epithelium were generated. Validity, repeatability, and utility of the method were established. Results. En face OCT images generated by manual and automatic segmentation were nearly indistinguishable and displayed similar regions of SRF. En face OCT images displayed uniform intensities and similar retinal vascular patterns in a healthy subject, while the size and appearance of a hypopigmented fibrotic scar in an AMD subject were similar at 2 visits. In AMD subjects, dark regions on en face OCT images corresponded to reduced or absent light reflectance due to SRF. On en face OCT images, a decrease in SRF areas with treatment was demonstrated and this corresponded with a reduction in the central subfield retinal thickness. Conclusion. En face OCT imaging is a promising tool for visualization and monitoring of SRF area due to disease progression and treatment.
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.
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