title: Context-dependent enhancements for radiological images.
abstract: Many techniques have been proposed to enhance radiographic images and each of them may be optimal depending upon the circumstances. However, the problem confronting the radiologist or the physician is which enhancement to use and how to select the parameters when a specific feature is to be emphasized. At the University of Ottawa, our research work is oriented towards automatic context-dependent enhancements. Our approach attempts to match the three phases involved in viewing a radiograph: getting a global impression, analyzing the objects and the local features, and focusing on the image perturbations. In this article, we report on enhancements to support the first two phases in the case of chest radiographs and on the applicability of gray level reversal transformations.
authors.affiliation: Telecommunications Research Institute of Ontario, University of Ottawa Medical Communication Research Centre, Canada.
authors.forename: B
authors.initials: B
authors.lastname: Plessis
authors.forename: M
authors.initials: M
authors.lastname: Goldberg
authors.forename: R
authors.initials: R
authors.lastname: Dillon
authors.forename: J
authors.initials: J
authors.lastname: Tombaugh
authors.forename: J
authors.initials: J
authors.lastname: Robertson
authors.forename: G
authors.initials: G
authors.lastname: Bélanger
authors.forename: N
authors.initials: N
authors.lastname: Hickey
mesh_terms: D004636:Emergency Service, Hospital; D006321:Heart / Q000000981:diagnostic imaging; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008962:Models, Theoretical; D011856:Radiographic Image Enhancement / Q000379:methods*; D013902:Radiography, Thoracic; D011872:Radiology Department, Hospital; D012815:Signal Processing, Computer-Assisted; D013131:Spine / Q000000981:diagnostic imaging