Multiresolution Denoising for Optical Coherence Tomography: A Review and Evaluation

Abstract

Abstract:Recently emerging non-invasive imaging modality -optical coherence tomography (OCT) -is becoming an increasingly important diagnostic tool in various medical applications. One of its main limitations is the presence of speckle noise which obscures small and low-intensity features. The use of multiresolution techniques has been recently reported by several authors with promising results. These approaches take into account the signal and noise properties in different ways. Approaches that take into account the global orientation properties of OCT images apply accordingly different level of smoothing in different orientation subbands. Other approaches take into account local signal and noise covariance's.So far it was unclear how these different approaches compare to each other and to the best available single-resolution despeckling techniques. The clinical relevance of the denoising results also remains to be determined. In this paper we review systematically recent multiresolution OCT speckle filters and we report the results of a comparative experimental study.We use 15 different OCT images extracted from five different three-dimensional volumes, and we also generate a software phantom with real OCT noise. These test images are processed with different filters and the results are evaluated both visually and in terms of different performance measures. The results indicate significant differences in the performance of the analyzed methods. Wavelet techniques perform much better than the single resolution ones and some of the wavelet methods improve remarkably the quality of OCT images.

Application of Independent Component Analysis techniques in speckle noise reduction of retinal OCT images

“…This includes spatial [4], angular [5], polarization [6] and frequency [7] compounding techniques. As for post-processing, anisotropic diffusion-based techniques [3] and multi-scale/multi-resolution geometric representation techniques [8] are of high interest between scholars. Use of compressive sensing and sparse representation have also been explored in the past few years [9].…”

Directional Weight Based Contourlet Transform Denoising Algorithm for Oct Image

“…Several algorithms have been proposed in some literatures to handle the speckle noise. The denoising effect of the spatial domain methods, such as Lee filter and RKT filter which blurs the edge easily while dealing with an image of OCT, is not satisfied [1,2]. In the frequency domain methods, Adler et al first used the adaptive wavelet threshold denoising algorithm to realize the OCT retinal image denoising [3].…”

“…However, the algorithm will be invalid while handling the images of multi-direction. The review of the OCT image denoising methods indicated that the wavelet-based threshold denoising algorithm does better in OCT image denoising while preserving edges [1] [4].…”

Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform

“…[4][5][6][7] Adler et al, Wagner et al, and Mayer et al, applied the discrete wavelet transform which provides the most compact representation, however, it has two main limitations which include the fact that it is not shift-invariant and not oriented in two dimensions. 4,[6][7][8] The dual-tree complex wavelet transform overcomes these limitations.…”