Sparsity based simplification of spectral domain optical coherence tomography images of cardiac samples
We propose a sparsity based simplification method for Spectral Domain Optical Coherence Tomography (SD OCT) images of cardiac samples, displaying layers of tissue. Inspired by the Compressed Sensing (CS) theory, we implement a dedicated sparse sampling of SD OCT samples achieving image simplification suited for layers segmentation, which is the target application. We compare our simplified images to state of the art denoising approaches. We also validate a straightforward segmentation approach on the variance map of the simplified images against manual delineation on raw SD OCT images of in vitro biological samples from four human hearts. Finally, we correlate average layer thickness with histopatho logical measures.
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)