An efficient energy-density-dependent undersampling approach for compressive sensing in spectral domain optical coherence tomography (Conference Presentation)

Sanjukta N. Bose; Jin U. Kang

doi:10.1117/12.2251401

24 April 2017 An efficient energy-density-dependent undersampling approach for compressive sensing in spectral domain optical coherence tomography (Conference Presentation)

Sanjukta N. Bose, Jin U. Kang

Author Affiliations +

Proceedings Volume 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II; 100760H (2017) https://doi.org/10.1117/12.2251401
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

Many prior studies performed in the area of compressive optical coherence tomography (OCT) have mostly dealt with the problem of compressive sensing and sparse recovery of processed OCT images. Unlike these studies, in this paper, we study the application of compressive sensing in terms of efficient data storage and generating OCT images from undersampled raw unprocessed spectral domain OCT data. High resolution spectral domain OCT requires acquisition of enormous amount of data at very high sampling rate but such a large amount of the raw data impedes fast and efficient data storage and communication. To solve the problem of storing a large amount of data, we propose a specific undersampling method guided by the energy density of the spectral domain data in order to facilitate sparse representation of the raw data in terms of its salient frequency domain samples. This method takes into account not just the higher amplitude spectral data, as suggested in some previous studies but samples data based on nearly uniform distribution of energy over all the sampling intervals in the entire spectrum. Finally, we apply some state of the art sparse recovery methods involving L1 minimization to recover our desired high resolution images from the undersampled spectral domain data. We demonstrate the performance of our proposed scheme by comparing it with the recovery accuracy of some recent energy-guided undersampling methods and the conventional compressive sensing with random undersampling. We also compare the performance of our method with the other methods in terms of data compression ratio with respect to the reconstruction error.

Conference Presentation

Citation Download Citation

Sanjukta N. Bose and Jin U. Kang "An efficient energy-density-dependent undersampling approach for compressive sensing in spectral domain optical coherence tomography (Conference Presentation)", Proc. SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II, 100760H (24 April 2017); https://doi.org/10.1117/12.2251401

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Optical coherence tomography

Compressed sensing

Data storage

Data communications

Data acquisition

Spectral resolution

Data compression

Show All Keywords

Keywords/Phrases

Search In:

Publication Years