Abstract
Improving the spatial resolution of Optical Coherence Tomography (OCT) images is important for the visualization and analysis of small morphological features in biological tissue such as blood vessels, membranes, cellular layers, etc. In this paper, we propose a novel reconstruction approach to obtaining super-resolved OCT tomograms from multiple lower resolution images. The proposed Multi-Penalty Conditional Random Field (MPCRF) method combines four different penalty factors (spatial proximity, first and second order intensity variations, as well as a spline-based smoothness of fit) into the prior model within a Maximum A Posteriori (MAP) estimation framework. Test carried out in retinal OCT images illustrate the effectiveness of the proposed MPCRF reconstruction approach in terms of spatial resolution enhancement, as compared to previously published super resolved image reconstruction methods. Visual assessment of the MPCRF results demonstrate the potential of this method in better preservation of fine details and structures of the imaged sample, as well as retaining the sharpness of biological tissue boundaries while reducing the effects of speckle noise inherent to OCT. Quantitative evaluation using imaging metrics such as Signal-to-Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Equivalent Number of Looks (ENL), and Edge Preservation Parameter show significant visual quality improvement with the MPCRF approach. Therefore, the proposed MPCRF reconstruction approach is an effective tool for enhancing the spatial resolution of OCT images without the necessity for significant imaging hardware modifications.
Highlights
Optical Coherence Tomography (OCT) is an interferometric imaging technique that allows for non-invasive visualization of the structural characteristics of highly scattering objects such as biological tissues at depths of 1-2 mm below the tissue [1]
Our proposed Multi-Penalty Conditional Random Field (MPCRF) method is an enhanced version of Total Variation (TV) approach that accounts for four different regularization terms in its formulation to compensate for the weakness of tractional TV
We believe that this improves the efficiency of the proposed MPCRF method compare to the conventional TV and Kernel Based Interpolation (KBI) approach in terms of spatial resolution enhancement
Summary
Optical Coherence Tomography (OCT) is an interferometric imaging technique that allows for non-invasive visualization of the structural characteristics of highly scattering objects such as biological tissues at depths of 1-2 mm below the tissue [1]. Given the importance of spatial resolution and image quality for visualization and analysis of fine structural features in the imaged objects, novel methods for enhancing the spatial resolution in OCT images are highly desired. The presence of speckle noise in the OCT images, which is an inherent characteristic of any interferometric imaging technique, gives the images a grainy appearance and limit the spatial resolution of the imaging method [4,5,6,7]. All of the above mentioned factors can case degradation of the spatial OCT resolution which could obscure important fine morphological features in the imaged sample, making any further image processing and analysis such as segmentation, pattern recognition, etc., challenging [1]
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