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Application of the EM algorithm to radiographic images
Author(s) -
Brailean James C.,
Little Darnell,
Giger Maryellen L.,
Chen ChinTu,
Sullivan Barry J.
Publication year - 1992
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.596895
Subject(s) - unsharp masking , algorithm , image quality , expectation–maximization algorithm , signal to noise ratio (imaging) , artificial intelligence , metric (unit) , observer (physics) , computer science , iterative reconstruction , computer vision , image restoration , mathematics , image processing , image (mathematics) , maximum likelihood , physics , statistics , operations management , economics , quantum mechanics
The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a “perceived” signal‐to‐noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye–brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.