Quantitative Evaluation of Transform Domains for Compressive Sampling-Based Recovery of Sparsely Sampled Volumetric OCT Images

Abstract

Recently, compressive sampling has received significant attention as an emerging technique for rapid volumetric imaging. We have previously investigated volumetric optical coherence tomography (OCT) image acquisition using compressive sampling techniques and showed that it was possible to recover image volumes from a subset of sampled images. Our previous findings used the multidimensional wavelet transform as the domain of sparsification for recovering OCT image volumes. In this report, we analyzed and compared the potential and efficiency of three other image transforms to reconstruct the same volumetric OCT image. Two quantitative measures, the mean square error and the structural similarity index, were applied to compare the quality of the reconstructed volumetric images. We observed that fast Fourier transformation and wavelet both are capable of reconstructing OCT image volumes for the orthogonal sparse sampling masks used in this report, but with different merits.

Data Analysis and Signal Postprocessing for Optical Coherence Tomography

“…This method has been demonstrated with both B-mode and 3-D imaging in real-time systems [28]. One study evaluated the performance of different transform domains for recovery of OCT volumes from sparse data sets [30]. The Fourier basis, wavelet basis, Dirac basis, discrete-cosine basis (DCT), and surfacelet basis are examples of common bases examined.…”

Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

“…Basis functions can be chosen from a set of training images similar to the input image [31] and thus can be more adaptive for the representation of specific features. Several recent works have also applied the sparse representation to OCT image reconstruction problems [4, 6,12, 14–16, 24, 32–34]. While different retinal layers have varied pathologic structures [35–37] and even speckle patterns, most of the sparse reconstruction methods only train one general dictionary to represent complex structures and textures in the ocular OCT images.…”