Abstract
PURPOSE: Spectral-domain optical coherent tomography (SD-OCT) is a useful tool for visualizing, treating, and monitoring retinal abnormality in patients with different retinal diseases. However, the assessment of SD-OCT images is thwarted by the lack of image quality necessary for ophthalmologists to analyze and quantify the diseases. This has hindered the potential role of hyperreflective foci (HRF) as a prognostic indicator of visual outcome in patients with retinal diseases. We present a new multi-vendor algorithm that is robust to noise while enhancing the HRF in SD-OCT images. METHODS: The proposed algorithm processes the SD-OCT images in two parallel processes simultaneously. The two parallel processes are combined by histogram matching. An inverse of both logarithmic and orthogonal transforms is applied to the mapped data to produce the enhanced image. RESULTS: We evaluated our algorithm on a dataset composed of 40 SD-OCT volumes. The proposed method obtained high values for the measure of enhancement, peak signal-to-noise ratio, structure similarity, and correlation (ρ) and a low value for mean square error of 36.72, 38.87, 0.87, 0.98, and 25.12 for Cirrus; 40.77, 41.84, 0.89, 0.98, and 22.15 for Spectralis; and 30.81, 32.10, 0.81, 0.96, and 28.55 for Topcon SD-OCT devices, respectively. CONCLUSIONS: The proposed algorithm can be used in the medical field to assist ophthalmologists and in the preprocessing of medical images. TRANSLATIONAL RELEVANCE: The proposed enhancement algorithm facilitates the visualization and detection of HRF, which is a step forward in assisting clinicians with decision making about patient treatment planning and disease monitoring.