We present a novel scheme for blind suppression of noise from a sequence of optical coherence tomography (OCT) images, such as those collected on a real-time OCT imaging system. In contrast to virtually all existing approaches to OCT denoising, our technique is specifically aimed at collections of images and is able to exploit the correlations among those images. The proposed method approximates the optimal linear denoising operator for log-transformed data using a 2-D discrete wavelet transform (DWT) to decorrelate in space and the discrete Fourier transform (DFT), or an estimated transform, to decorrelate in time. Decorrelated coefficients are then denoised and converted back to the image domain to produce denoised OCT images. Real-time OCT data processed with this technique shows significant reduction in noise
"Much work has been performed on OCT speckle reduction using compounding techniques  . Although such techniques reduce speckle, they involve hardware modifications that can be expensive and inconvenient. "
[Show abstract][Hide abstract] ABSTRACT: This paper presents a new directional Bayesian despeckling technique for optical coherence tomography (OCT) images in the complex wavelet domain, which reduces speckle while preserving the detailed features and textural information. It has been shown that wavelet coefficients of natural images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions. On the other hand, most of the edge information of layer boundaries in OCT images is located in the same direction. For these directional images, the use of heavy-tailed distributions does not seem to be appropriate for all wavelet decomposition subbands. So wavelet coefficients of the subbands which have almost the same orientation as the original image are modeled with heavy-tailed distributions such as the Cauchy, while the others are modeled with a simple Gaussian distribution. Within this framework, we design a maximum a posteriori estimator to remove speckle from noisy coefficients. Better results are obtained when we use the dual-tree complex wavelet transform which offers improved directional selectivity and near shift invariance property. Our results show that the proposed scheme outperforms some existing despeckling methods.
Electrical Engineering, 2007. ICEE '07. International Conference on; 05/2007
[Show abstract][Hide 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.
Current Medical Imaging Reviews 10/2008; 4(4):270-284. DOI:10.2174/157340508786404044 · 0.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We address the issue of noise robustness of reconstruction techniques for frequency-domain optical-coherence tomography (FDOCT). We consider three reconstruction techniques: Fourier, iterative phase recovery, and cepstral techniques. We characterize the reconstructions in terms of their statistical bias and variance and obtain approximate analytical expressions under the assumption of small noise. We also perform Monte Carlo analyses and show that the experimental results are in agreement with the theoretical predictions. It turns out that the iterative and cepstral techniques yield reconstructions with a smaller bias than the Fourier method. The three techniques, however, have identical variance profiles, and their consistency increases linearly as a function of the signal-to-noise ratio.
IEEE Transactions on Signal Processing 04/2010; 58(3-58):1947 - 1951. DOI:10.1109/TSP.2009.2037077 · 2.79 Impact Factor
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