Conference Paper

Multi-Dimensional Denoising of Real-Time Oct Imaging Data

Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL
DOI: 10.1109/ICASSP.2006.1660551 Conference: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, Volume: 2
Source: IEEE Xplore

ABSTRACT

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

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    • "Much work has been performed on OCT speckle reduction using compounding techniques [2] [3]. Although such techniques reduce speckle, they involve hardware modifications that can be expensive and inconvenient. "
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    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.
    Full-text · Conference Paper · May 2007
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    ABSTRACT: The quality of images obtained with Optical Coherence Tomography (OCT), like many other imaging modalities, can be enhanced by using signal processing to numerically infer properties of the object being studied. While a great deal of insight can be gained by understanding OCT intuitively as a range-finding mechanism, more sophisticated analysis can reveal additional detail and features to the extent data quality allows. To maximize the utility of the data, signal processing is used to reject noise and to ensure the resulting image conforms to known properties of the object. We briefly summarize concepts of inference in signal processing. These ideas are applied when reviewing and examining methods of reducing noise, improving resolution through deconvolution, reducing speckle, correcting for material dispersion and OCT system imperfections, and deblurring the defocusing effects outside of the depth-of-field of the OCT instrument.
    No preview · Chapter · Dec 2007
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    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.
    Full-text · Article · Oct 2008 · Current Medical Imaging Reviews
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