2013
DOI: 10.1109/tmi.2013.2271904
|View full text |Cite
|
Sign up to set email alerts
|

Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

Abstract: In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not r… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
78
0

Year Published

2015
2015
2023
2023

Publication Types

Select...
7
1

Relationship

2
6

Authors

Journals

citations
Cited by 200 publications
(82 citation statements)
references
References 63 publications
0
78
0
Order By: Relevance
“…To obtain [ M ] we use ridge regression that mimimizes an objective function given by (Leyuan Fang et al, 2013; Jia et al, 2013):…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…To obtain [ M ] we use ridge regression that mimimizes an objective function given by (Leyuan Fang et al, 2013; Jia et al, 2013):…”
Section: Methodsmentioning
confidence: 99%
“…Our method uses a combination of Proper Orthogonal Decomposition (POD)(Kerschen et al, 2005; Kosambi, 1943; Karhunen, 1947; Loeve, 1948) and ridge regression (Fang et al, 2013) to merge patient-specific CFD and 4D Flow MRI. Benchmark tests using a numerical flow phantom against state-of-the-art techniques indicates that our method is able to recover fine details in complex recirculating flows with error metrics that are substantially better.…”
Section: Introductionmentioning
confidence: 99%
“…9,12 The images were post-processed with the sparsity based simultaneous denoising and interpolation method trained specifically for these imaging sytems. 17 Sparsity based simultaneous denoising and interpolation has previously been used for reliable denoising of OCT images in human and murine eyes without distorting image features. 17,18 The location of the scans was confirmed with the scanning laser ophthalmoscope infrared images from the Spectralis or the summed voxel projection from the intraoperative images.…”
Section: Methodsmentioning
confidence: 99%
“…17 Sparsity based simultaneous denoising and interpolation has previously been used for reliable denoising of OCT images in human and murine eyes without distorting image features. 17,18 The location of the scans was confirmed with the scanning laser ophthalmoscope infrared images from the Spectralis or the summed voxel projection from the intraoperative images. The locations of preoperative connecting strands for each subject were matched and compared with corresponding locations in intraoperative and postoperative images.…”
Section: Methodsmentioning
confidence: 99%
“…We began the segmentation process by denoising the 8-bit grayscale OCT images using a previously described sparsitybased denoising technique. 32 Next, we calculated three gradient images, two horizontal gradient images, and one vertical gradient image by convolving the image with [1;1; 1;1; 1;0; −1; −1; −1; −1; −1], [−1; −1; −1; −1; −1;0; 1;1; 1;1; 1], and [1; 1; 1; 1; 1; 0; −1; −1; −1; −1; −1] (MATLAB notation) filters. We then linearly normalized the pixel values in these three gradient images to be between 0 and 1.…”
Section: Representative Application In Retinal Optical Coherence Tomomentioning
confidence: 99%