Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints

Jun Ma*

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

The total variation smoothing methods are common in image processing due to its remarkable ability to preserve edges. Its application in medical image reconstruction has also being addressed by several researchers. The corresponding reconstruction algorithms developed, however, either lack considerations of the positivity constraint usually imposed on medical images, or are not flexible enough to be extended to different imaging modalities or to different noise distributions. In this paper we adopt the recently developed multiplicative iterative algorithm to produce an algorithm for total variation medical image reconstruction. The advantage of this algorithm is that it is easily extendable to different image noise models and to different imaging modalities. Moreover, it respects the positivity constraint.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1774-1777
Number of pages4
ISBN (Electronic)9781424441280
ISBN (Print)9781424441273
DOIs
Publication statusPublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Other

Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
CountryUnited States
CityChicago, IL
Period30/03/112/04/11

Fingerprint Dive into the research topics of 'Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints'. Together they form a unique fingerprint.

Cite this