Denoising PET images using singular value thresholding and Stein's unbiased risk estimate

Ulas Bagci*, Daniel J. Mollura

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET images are low resolution; therefore, it is challenging to denoise them while preserving structural information. To address these two difficulties, we introduce a novel methodology for denoising PET images. The proposed method uses the singular value thresholding concept and Stein's unbiased risk estimate to optimize a soft thresholding rule. Results, obtained from 40 MRI-PET images, demonstrate that the proposed algorithm is able to denoise PET images successfully, while still maintaining the quantitative information.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages115-122
Number of pages8
EditionPART 3
DOIs
StatePublished - 2013
Externally publishedYes
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/TerritoryJapan
CityNagoya
Period9/22/139/26/13

Keywords

  • Denoising
  • PET
  • Singular Value Thresholding
  • Stein Risk Estimate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Denoising PET images using singular value thresholding and Stein's unbiased risk estimate'. Together they form a unique fingerprint.

Cite this