Home

De-noising SPECT/PET Images Using Cross-Scale Regularization

Yinpeng Jin; Elsa D. Angelini; Peter D. Esser; Andrew F. Laine

Title:
De-noising SPECT/PET Images Using Cross-Scale Regularization
Author(s):
Jin, Yinpeng
Angelini, Elsa D.
Esser, Peter D.
Laine, Andrew F.
Date:
Type:
Articles
Department:
Biomedical Engineering
Permanent URL:
Notes:
Medical image computing and computer-assisted intervention - MICCAI 2003 : 6th International Conference, Montréal, Canada, November 2003 : proceedings ; Lecture Notes in Computer Science, Volume 2879 (Berlin ; New York : Springer-Verlag, 2003), pp. 32-40.
Abstract:
De-noising of SPECT and PET images is a challenging task due to the inherent low signal-to-noise ratio of acquired data. Wavelet based multi-scale denoising methods typically apply thresholding operators on sub-band coefficients to eliminate noise components in spatial-frequency space prior to reconstruction. In the case of high noise levels, detailed scales of sub-band images are usually dominated by noise which cannot be easily removed using traditional thresholding schemes. To address this issue, a cross-scale regularization scheme is introduced, which takes into account cross-scale coherence of structured signals. Preliminary results show promising performance in denoising clinical SPECT and PET images for liver and brain studies. Wavelet thresholding was also compared to denoising with a brushlet expansion. The proposed regularization scheme eliminates the need for threshold parameter settings, making the denoising process less tedious and suitable for clinical practice.
Subject(s):
Biomedical engineering
Publisher DOI:
http://dx.doi.org/10.1007/978-3-540-39903-2_5
Item views:
162
Metadata:
View

In Partnership with the Center for Digital Research and Scholarship at Columbia University Libraries/Information Services.