title: Application of the EM algorithm to radiographic images.
abstract: 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.
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois 60208.
authors.forename: J C
authors.initials: JC
authors.lastname: Brailean
authors.forename: D
authors.initials: D
authors.lastname: Little
authors.forename: M L
authors.initials: ML
authors.lastname: Giger
authors.forename: C T
authors.initials: CT
authors.lastname: Chen
authors.forename: B J
authors.initials: BJ
authors.lastname: Sullivan
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008433:Mathematics; D008962:Models, Theoretical; D015588:Observer Variation; D014055:Tomography, Emission-Computed / Q000379:methods