Adaptive filtering of optical coherent tomography fringe data with ensemble empirical mode decomposition

Gangjun Liu; Jun Zhang; Lingfeng Yu; Zhongping Chen

doi:10.1117/12.841189

19 February 2010 Adaptive filtering of optical coherent tomography fringe data with ensemble empirical mode decomposition

Gangjun Liu, Jun Zhang, Lingfeng Yu, Zhongping Chen

Author Affiliations +

Proceedings Volume 7554, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XIV; 75542U (2010) https://doi.org/10.1117/12.841189
Event: SPIE BiOS, 2010, San Francisco, California, United States

Abstract

Empirical mode decomposition (EMD) is a new adaptive data analysis method in which the analyzed data is decomposed into a limited number of intrinsic mode functions (IMFs) through a sifting process. One problem with EMD is mode mixing, which has been solved by Wu et al using ensemble EMD (EEMD). In this paper, we applied the EEMD method to data acquired from optical coherence tomography (OCT) to improve the image quality. First, the original OCT fringe data is converted from linear wavelength to linear frequency through a calibration process. Second, the calibrated data is decomposed into different IMFs by EEMD. Third, the physical meaning of different IMFs was analyzed. Fourth, IMFs that represented noise were removed from the calibrated fringe data. The noise removed fringe data was then Fourier transformed to get depth information. EEMD was found to be able to separate different frequency noise into different IMFs. The signal to noise ratio of OCT image was improved by removing the IMFs that represent noise from the acquired fringe data.

Citation Download Citation

Gangjun Liu, Jun Zhang, Lingfeng Yu, and Zhongping Chen "Adaptive filtering of optical coherent tomography fringe data with ensemble empirical mode decomposition", Proc. SPIE 7554, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XIV, 75542U (19 February 2010); https://doi.org/10.1117/12.841189

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