2019/03/23 by Max-Heinrich Laves, Sontje Ihler, Laves, Max-Heinrich +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.1903.09809
Accepted for publication at the SPIE/OSA European Conferences on Biomedical Optics (ECBO) 2019
arxiv created 2019/03/23 · arxiv updated 2019/03/26
Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis. In this paper, a denoising approach that preserves disease characteristics on retinal optical coherence tomography images in ophthalmology is presented. By combining a deep convolutional autoencoder with a priorly trained ResNet image classifier as regularizer, the perceptibility of delicate details is encouraged and only information-less background noise is filtered out. With our approach, higher peak signal-to-noise ratios with PSNR = 31.2 dB and higher classification accuracy of ACC = 85.0 % can be achieved for denoised images compared to state-of-the-art denoising with PSNR = 29.4 dB or ACC = 70.3 %, depending on the method. It is shown that regularized autoencoders are capable of denoising retinal OCT images without blurring details of diseases.