Paper Info
Title
Sparsity-Based Simplification Of Spectral-Domain Optical Coherence Tomography Images Of Cardiac Samples
Abstract
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 histopathological measures.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493286
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Keywords
Field
DocType
Optical Coherence Tomography, Sparse sampling, Image simplification, Image segmentation
Noise reduction,Computer vision,Optical coherence tomography,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Sampling (statistics),Compressed sensing
Conference
ISSN
Citations 
PageRank 
1945-7928
0
0.34
References 
Authors
7
6
Name
Order
Citations
PageRank
William Meiniel111.40
Yu Can200.34
Christine P Hendon361.66
Jean-Christophe Olivo-Marin474777.94
Andrew F. Laine574783.01
Elsa D. Angelini674060.44