3-D Adaptive Sparsity Based Image Compression With Applications to Optical Coherence Tomography

Abstract

We present a novel general-purpose compression method for tomographic images, termed 3D adaptive sparse representation based compression (3D-ASRC). In this paper, we focus on applications of 3D-ASRC for the compression of ophthalmic 3D optical coherence tomography (OCT) images. The 3D-ASRC algorithm exploits correlations among adjacent OCT images to improve compression performance, yet is sensitive to preserving their differences. Due to the inherent denoising mechanism of the sparsity based 3D-ASRC, the quality of the compressed images are often better than the raw images they are based on. Experiments on clinical-grade retinal OCT images demonstrate the superiority of the proposed 3D-ASRC over other well-known compression methods.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…For the study of many retinal diseases, accurate quantification of layer thicknesses in the acquired OCT images is crucial to advance our understanding of such factors as disease severity and pathogenic processes, and to identify potential biomarkers of disease progression. Moreover, segmentation of retinal layer boundaries is the first step in creating vascular pattern images from the popular new OCT angiography imaging modalities [10][11][12][13]. Since manual segmentation of OCT images is time consuming and subjective, it is necessary to develop automatic layer segmentation algorithms.…”

Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

“…Next, we use the average patches for the sparse reconstruction. Since the 3-D OCT image also has strong correlations among nearby slices [4, 32], we simultaneously process the averaged patches from the same position of nearby slices (called as the nearby patches ${\left\{{\mathrm{boldx}}_{i,t}^{\mathit{Ave},r}\right\}}_{t=1}^T$, where T is number of nearby patches) with a joint sparse decomposition technique. This technique decomposes the nearby patches on the same dictionary atom with different coefficient values, which can enhance the decomposition efficiency.…”

“…Finally, as in [32], a weighted average operation is conducted on the nearby patches and then these recovered nearby patches are returned to the original positions to reconstruct the denoised nearby images.…”

“…As in [32], we also conduct a weighted average operation on the nearby patches, and return the set of nearby patches ${\left\{{\hat{\mathbf{x}}}_{H,i,t}^r\right\}}_{t=1}^T$ to the original positions to reconstruct the high resolution nearby 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.…”