Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17011
Title: Multispectral Photoacoustic Imaging Artifact Removal and Denoising Using Time Series Model-Based Spectral Noise Estimation
Authors: Kazakeviciute, A
Ho, CJH
Olivo, M
Keywords: Photoacoustic imaging;artifact removal;denoising;Wiener filtering;Fourier domain;PSD estimation
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Citation: IEEE Transactions on Medical Imaging, 2016, 35 (9), pp. 2151 - 2163
Abstract: The aim of this study is to solve a problem of denoising and artifact removal from in vivo multispectral photoacoustic imaging when the level of noise is not known a priori. The study analyzes Wiener filtering in Fourier domain when a family of anisotropic shape filters is considered. The unknown noise and signal power spectral densities are estimated using spectral information of images and the autoregressive of the power 1 (AR(1) model. Edge preservation is achieved by detecting image edges in the original and the denoised image and superimposing a weighted contribution of the two edge images to the resulting denoised image. The method is tested on multispectral photoacoustic images from simulations, a tissue-mimicking phantom, as well as in vivo imaging of the mouse, with its performance compared against that of the standard Wiener filtering in Fourier domain. The results reveal better denoising and fine details preservation capabilities of the proposed method when compared to that of the standard Wiener filtering in Fourier domain, suggesting that this could be a useful denoising technique for other multispectral photoacoustic studies.
URI: http://bura.brunel.ac.uk/handle/2438/17011
DOI: http://dx.doi.org/10.1109/TMI.2016.2550624
ISSN: 0278-0062
http://dx.doi.org/10.1109/TMI.2016.2550624
1558-254X
Appears in Collections:Dept of Mathematics Research Papers

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