Stopping Rule for the MLE Algorithm Based on Statistical Hypothesis Testing.

Abstract:

:It is known that when the maximum likelihood estimator (MLE) algorithm passes a certain point, it produces images that begin to deteriorate. We propose a quantitative criterion with a simple probabilistic interpretation that allows the user to stop the algorithm just before this effect begins. The MLE algorithm searches for the image that has the maximum probability to generate the projection data. The underlying assumption of the algorithm is a Poisson distribution of the data. Therefore, the best image, according to the MLE algorithm, is the one that results in projection means which are as close to the data as possible. It is shown that this goal conflicts with the assumption that the data are Poisson-distributed. We test a statistical hypothesis whereby the projection data could have been generated by the image produced after each iteration. The acceptance or rejection of the hypothesis is based on a parameter that decreases as the images improve and increases as they deteriorate. We show that the best MLE images, which pass the test, result in somewhat lower noise in regions of high activity than the filtered back-projection results and much improved images in low activity regions. The applicability of the proposed stopping rule to other iterative schemes is discussed.

journal_name

IEEE Trans Med Imaging

authors

Veklerov E,Llacer J

doi

10.1109/TMI.1987.4307849

subject

Has Abstract

pub_date

1987-01-01 00:00:00

pages

313-9

issue

4

eissn

0278-0062

issn

1558-254X

journal_volume

6

pub_type

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