Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

Abstract

We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.

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.…”

Statistical model for OCT image denoising

“…Fang et al [12] later introduce a single-frame version that does not require a priori high-quality frame, instead utilizing a previously collected datasets. A segmentation step is further included to form a segmentation based sparse reconstruction (SSR) framework which is specifically designed for retinal OCT images [13].…”

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy

“…Furthermore, prior to the segmentation procedure, all images were pre-processed for speckle noise reduction by the probability-based non-local means filter [36]. Although there are many denoising methods that had good performance, such as sparsity based denoising [45,46], we choose this method as it is competitive with other stateof-the-art speckle removal techniques and able to accurately preserve edges and structural details with small computational cost. Denoising methods such as sparsity based denoising could be an option in our framework and will be studied in the future.…”