Speckle denoising in optical coherence tomography images using residual deep convolutional neural network

Abstract

Optical Coherence Tomography (OCT) is an emerging imaging modality used for diagnosis of ocular diseases like age-related macular degeneration (AMD) and macular edema. OCT imaging is a non-invasive technique to capture cross-sectional volumes of the retinal areas of human eye. Due to coherent nature of image acquisition process, OCT images suffer from granular multiplicative speckle noise. Presence of speckle noise in OCT images makes its clinical analysis difficult for the experts. The same is the problem with the development of computer aided diagnosis (CAD) systems for detection of ocular diseases. Speckle noise is granular in nature and interferes with the diagnostic observations made using OCT images and the segmentation of different OCT layers. This work presents an efficient OCT denoising technique using residual convolutional neural network. The proposed technique will not only help experts in analysis of OCT images, but can also act as first step to construct CAD systems for ocular diseases. The performance of the proposed approach is evaluated on Duke (SD-OCT) and Topcon (3D-OCT) image databases based on visual and parametric observations. The performance of the proposed method on parameters like PSNR, SSIM, MSR, CNR, and ENL is compared with the state-of-the-art speckle denoising methods. It is observed that the proposed approach performs better as compared to the methods referred from literature on both visual and parametric evaluations.