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Proceedings Paper

Automated wavelet denoising of photoacoustic signals for burn-depth image reconstruction
Author(s): Scott H. Holan; John A. Viator
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Paper Abstract

Photoacoustic image reconstruction involves dozens or perhaps hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a sample with laser light are used to produce an image of the acoustic source. Each of these point measurements must undergo some signal processing, such as denoising and system deconvolution. In order to efficiently process the numerous signals acquired for photoacoustic imaging, we have developed an automated wavelet algorithm for processing signals generated in a burn injury phantom. We used the discrete wavelet transform to denoise photoacoustic signals generated in an optically turbid phantom containing whole blood. The denoising used universal level independent thresholding, as developed by Donoho and Johnstone. The entire signal processing technique was automated so that no user intervention was needed to reconstruct the images. The signals were backprojected using the automated wavelet processing software and showed reconstruction using denoised signals improved image quality by 21%, using a relative 2-norm difference scheme.

Paper Details

Date Published: 13 February 2007
PDF: 6 pages
Proc. SPIE 6437, Photons Plus Ultrasound: Imaging and Sensing 2007: The Eighth Conference on Biomedical Thermoacoustics, Optoacoustics, and Acousto-optics, 643719 (13 February 2007); doi: 10.1117/12.701228
Show Author Affiliations
Scott H. Holan, Univ. of Missouri/Columbia (United States)
John A. Viator, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 6437:
Photons Plus Ultrasound: Imaging and Sensing 2007: The Eighth Conference on Biomedical Thermoacoustics, Optoacoustics, and Acousto-optics
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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