Using a dual-tree complex wavelet transform for denoising an optical coherence tomography angiography blood vessel image

Abstract

High image quality is of great importance for precise diagnosis and therapeutics of eye disease in clinic. A human retina OCT angiography (OCTA) image can be extracted from multiple OCT B-scans to visualize the distribution of blood vessels. However, OCTA suffer from the degeneration of image quality due to inherent Gaussian noise of the OCT system while the blood vessel’s signal is extracted. The degeneration of the noise in OCTA image will be more conducive to the evaluation of abnormal and normal blood vessels in the human eye. To precisely assist diagnosis and therapeutics in clinic by reducing the Gaussian noise in the OCTA image, an OCTA image denoising method is proposed based on the dual-tree complex wavelet transform and bilateral shrinking Bayes frame. Initially, OCTA images are extracted from the raw data based on the optical microangiography algorithm. Then, the image is decomposed into the wavelet domain using the dual-tree complex wavelet transform. The signal and noise among different wavelet scale layers are separated on the basis of the Bayesian posterior probability. Finally, the inverse wavelet transform is employed to reconstruct the denoised image. Through the noise reduction process of the algorithm, the PSNR and CNR of the OCTA image are increased by 49.15% and 47.91%, respectively. According to the results, the wavelet transform can effectively separate the blood flow signal and noise in processing the OCTA signal, which will provide an effective image processing method for the clinical evaluation requiring high-quality OCTA images.