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High quality, large size volumetric imaging of biological tissue with optical coherence tomography (OCT) requires large number and high density of scans, which results in large data acquisition volume. This may lead to corruption of the data with motion artifacts related to natural motion of biological tissue, and could potentially cause conflicts with the maximum permissible exposure of biological tissue to optical radiation. Therefore, OCT can benefit greatly from different approaches to sparse or compressive sampling of the data where the signal is recovered from its sub-Nyquist measurements. In this paper, a new energy-guided compressive sensing approach is proposed for improving the quality of images acquired with Fourier domain OCT (FD-OCT) and reconstructed from sparse data sets. The proposed algorithm learns an optimized sampling probability density function based on the energy distribution of the training data set, which is then used for sparse sampling instead of the commonly used uniformly random sampling. It was demonstrated that the proposed energy-guided learning approach to compressive FD-OCT of retina images requires 45% fewer samples in comparison with the conventional uniform compressive sensing (CS) approach while achieving similar reconstruction performance. This novel approach to sparse sampling has the potential to significantly reduce data acquisition while maintaining image quality.

The resolution in optical coherence tomography imaging is an important parameter which determines the size of the smallest features that can be visualized. Sparse sampling approaches have shown considerable promise in producing high resolution OCT images with fewer camera pixels, reducing both the cost and the complexity of an imaging system. In this paper, we propose a non-local approach to the reconstruction of high resolution OCT images from sparsely sampled measurements. An iterative strategy is introduced for minimizing a homotopic, non-local regularized functional in the spatial domain, subject to data fidelity constraints in the k-space domain. The novel algorithm was tested on human retinal, corneal, and limbus images, acquired in-vivo, demonstrating the effectiveness of the proposed approach in generating high resolution reconstructions from a limited number of camera pixels., (© 2012 Optical Society of America)

An important image post-processing step for optical coherence tomography (OCT) images is speckle noise reduction. Noise in OCT images is multiplicative in nature and is difficult to suppress due to the fact that in addition the noise component, OCT speckle also carries structural information about the imaged object. To address this issue, a novel speckle noise reduction algorithm was developed. The algorithm projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach. The proposed algorithm was tested on a number of rodent (rat) retina images acquired in-vivo with an ultrahigh resolution OCT system. The performance of the algorithm was compared to that of the state-of-the-art algorithms currently available for speckle denoising, such as the adaptive median, maximum a posteriori (MAP) estimation, linear least squares estimation, anisotropic diffusion and wavelet-domain filtering methods. Experimental results show that the proposed approach is capable of achieving state-of-the-art performance when compared to the other tested methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation, and equivalent number of looks (ENL) measures. Visual comparisons also show that the proposed approach provides effective speckle noise suppression while preserving the sharpness and improving the visibility of morphological details, such as tiny capillaries and thin layers in the rat retina OCT images.

A computational model was developed to evaluate the limitations to the highest axial resolution, achievable with ultrahigh resolution optical coherence tomography (UHROCT) in the 1060 nm wavelength region for in-vivo imaging of the human and rodent retina. The model considers parameters such as the wavelength dependent water absorption, the average length of the human and rodent eyes, and the power limitations for the imaging beam as defined in the ANSI standard. A custom-built light source with re-shaped spectrum was used to verify experimentally the results from the computational model. Axial OCT resolution of 4.2 microm and 7.7 microm was measured from a mirror reflection with the custom light source by propagating the imaging beam through water cells with 5 mm and 25 mm thickness, corresponding to the average axial length of the rodent and human eye respectively. Assuming an average refractive index of 1.38 for retinal tissue, the expected axial OCT resolution in the rodent and human retina is 3 microm and 5.7 microm respectively. Retinal tomograms acquired in-vivo from the rat eye with the modified light source show clear visualization of all intraretinal layers, as well as a network of capillaries (approximately 10 microm in diameter) in the inner- and outer plexiform layers of the retina.

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

A novel speckle noise reduction algorithm based on a combination of Anisotropic Diffusion (AD) filtering and Interval Type-II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography (OCT) images. Unlike regular AD, the Interval Type-II fuzzy based AD algorithm considers the uncertainty in the calculated diffusion coefficient and appropriate adjustments to the coefficient are made. The new algorithm offers flexibility in optimizing the trade-off between the two image metrics: signal-to-noise (SNR) and Edginess, which are directly related to the structure of the imaged object. Application of the Interval Type-II fuzzy AD algorithm to OCT tomograms acquired in-vivo from a human finger tip and human retina show reduction in the speckle noise with very little edge blurring and about 13 dB and 7 dB image SNR improvement respectively. Comparison with Wiener, Adaptive Lee and regular Anisotropic Diffusion filters, applied to the same images, demonstrates the superior performance of the fuzzy Type-II AD algorithm in terms image SNR and edge preservation metrics improvement.

A novel speckle reduction technique based on soft thresholding of wavelet coefficients using interval type II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography images. The proposed algorithm is an extension of a recently published method for filtering additive Gaussian noise by use of type I fuzzy system. Unlike type I, interval type II fuzzy based thresholding filter considers the uncertainty in the calculated threshold and the wavelet coefficient is adjusted based on this uncertainty. A single parameter controls the signal-to-noise (SNR) improvement. Application of this novel algorithm to optical coherence tomograms acquired in-vivo from a human finger tip show reduction in the speckle noise with little edge blurring and image SNR improvement of about 10dB. Comparison with adaptive Wiener and adaptive Lee filters, applied to the same image, demonstrated the superior performance of the fuzzy type II algorithm in terms of image metrics improvement.