Computation tools for the Fourier transform infrared (FT-IR) spectroscopic imaging

Nguyen, Tan

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Description

Title

Computation tools for the Fourier transform infrared (FT-IR) spectroscopic imaging

Author(s)

Nguyen, Tan

Issue Date

2012-05-22T00:25:14Z

Director of Research (if dissertation) or Advisor (if thesis)

• Do, Minh N.
• Bhargava, Rohit

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Fourier transform infrared spectroscopy (FT-IR)
• denoising
• deblurring
• sparse reconstruction
• compressive sensing
• hyperspectral data

Abstract

Over the pass 60 years, infrared (IR) spectral imaging has become an important tool with various applications such as identifying chemical distributions of biomedically relevant components in tissue, investigating biomedical and biological processes, and studying development of diseases (histopathology). This thesis focuses on denoising and deblurring absorbance images of the Fourier transform infrared spectroscopy for improved spatial resolution while maintaining spectral quality. In addition, it aims to speed up data-acquisition by deploying state-of-the-art computational tools such as dictionary training for sparse representation, and compressive sensing. Here, we use a singular value decomposition denoising algorithm to recover the noiseless absorbance data. Then, novel variational Bayesian deconvolution algorithms using a theoretical formula of the optical point spread function (PSF) are used to improve the spatial resolution, and estimate the mismatching term in the true and theoretical PSF. For sparse reconstruction, we train a K-SVD dictionary to sparsely represent the interferograms. Then, using optimization algorithms, we recover the full dimensional interferograms from very few measurements. Using experimental results on the standard United States Air Force (USAF) 1951 target and breast tissue samples, we show an improvement of 10.53 dB in the signal-to-noise ratio (SNR) after denoising. In addition, the absorbance contrast ratio (ACR) is increased by at least 1.07 times after deblurring over a spatial frequency range of interest on the standard USAF target. Most importantly, our method improves the spatial resolution without significantly modifying the underlying spectral information. For sparse reconstruction, we demonstrate that reconstruction results with correlation factors of at least 0.999, and mean relative errors as small as 3% can be obtained by using just 32 measurements (1.9% of the total number of measurements).

Graduation Semester

2012-05

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http://hdl.handle.net/2142/31061

Copyright and License Information

Copyright 2012 Tan Nguyen

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