Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints

Jun Ma

doi:10.1109/ISBI.2011.5872750

Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints

Jun Ma^*

^*Corresponding author for this work

Department of Linguistics

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1774-1777
Number of pages	4
ISBN (Electronic)	9781424441280
ISBN (Print)	9781424441273
DOIs	https://doi.org/10.1109/ISBI.2011.5872750
Publication status	Published - 2011
Event	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: 30 Mar 2011 → 2 Apr 2011

Other

Other	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country	United States
City	Chicago, IL
Period	30/03/11 → 2/04/11

Access to Document

10.1109/ISBI.2011.5872750

Link to publication in Scopus

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Cite this

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title = "Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints",

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.",

author = "Jun Ma",

year = "2011",

doi = "10.1109/ISBI.2011.5872750",

language = "English",

isbn = "9781424441273",

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Ma, J 2011, Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints. in 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11., 5872750, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1774-1777, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, Chicago, IL, United States, 30/03/11. https://doi.org/10.1109/ISBI.2011.5872750

Total variation smoothed maximum penalized likelihood tomographic reconstruction with positivity constraints. / Ma, Jun.

2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2011. p. 1774-1777 5872750.

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

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