DocumentCode :
3493490
Title :
Reconstructing FT-IR spectroscopic imaging data with a sparse prior
Author :
Brady, Spencer P. ; Do, Minh N. ; Bhargava, Rohit
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana Champaign, Champaign, IL, USA
fYear :
2009
fDate :
7-10 Nov. 2009
Firstpage :
829
Lastpage :
832
Abstract :
Fourier Transform Infrared (FT-IR) spectroscopic imaging is a potentially valuable tool for diagnosing breast and prostate cancer, but its clinical deployment is limited due to long data acquisition times and vast storage requirements. To counter this limitation, we develop a sparse representation for FT-IR absorbance spectra using a learned dictionary. This sparse representation is used as prior knowledge in regularizing the compressed sensing inverse problem. The data size and acquisition time are directly proportional to the length of the measured signal, namely the interferogram. Hence, we model our measurement process as interferogram truncation, which we implement by low pass filtering and downsampling in the spectral domain. With a downsample factor of four, our reconstruction is adequate for tissue classification and provides a Peak Signal-to-noise Ratio (PSNR) of 41.92 dB, while standard interpolation of the same low resolution measurements can only provide a PSNR of 36.93 dB.
Keywords :
Fourier transform spectroscopy; biological tissues; cancer; data acquisition; interpolation; low-pass filters; medical image processing; FT-IR absorbance spectra; FT-IR spectroscopic imaging data; Fourier transform infrared spectroscopic imaging; acquisition time; breast cancer; compressed sensing inverse problem; data acquisition; data size; interferogram truncation; learned dictionary; low pass filtering; low resolution measurement; peak signal-to-noise ratio; prostate cancer; sparse prior; sparse representation; standard interpolation; storage requirements; tissue classification; Breast; Fourier transforms; Image reconstruction; Image storage; Infrared imaging; Infrared spectra; Optical imaging; PSNR; Prostate cancer; Spectroscopy; ℓ1-minimization; FT-IR; K-SVD;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
ISSN :
1522-4880
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2009.5414384
Filename :
5414384
Link To Document :
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