Enhancement and De-Noising of OCT Image by Adaptive Wavelet Thresholding Method

Abstract

This chapter proposed an adaptive wavelet thresholding method for enhancement and de-noising of retinal optical coherence tomography (OCT) image. Speckle noise degrades the OCT image and affects the disease diagnostic utility. OCT image enhancement is required for accurate analysis of inter and intra retinal layers. Enhancement is achieved through histogram mapping called Gaussianization transform. Further wavelet coefficients are modeled statistically to get the signal and noise information for finding the threshold value for weighing the wavelet coefficients. A Cauchy distribution is used to model the wavelet coefficients. An adaptive soft thresholding is used to estimate the true wavelet coefficients. Gaussianization transform widen the intensity range and enhances the OCT image and de-noising performances. Through different performance parameters, it is demonstrated that the proposed method outperforms the state-of-the-art methods. The proposed de-noising method has achieved 4.67% improvement in Peak Signal-to-Noise Ratio (PSNR), 2.61% in Structural Similarity (SSIM), 1.33% in Correlation coefficient (CoC) and 9.4% in Edge Preservation Index (EPI) parameters than the adaptive soft thresholding method, designed without statistical modeling.