title: Simulating the mammographic appearance of circumscribed lesions.
abstract: Optimization performance of digital image post-processing techniques in mammography requires controlled conditions of data sets permitting quantitative representation of image characteristics of pathological findings. Digital test objects, although objective and quantitative, do not mimic mammographic appearance and clinical data sets do not provide adequate sets of values of the various pathological finding characteristics. This can be overcome by digital simulation of pathological findings and superimposition on mammographic images. A simple method for simulation of mammographic appearance of radiopaque and/or radiolucent circumscribed lesions is presented. Circumscribed lesions are simulated using grey-level transformation functions which shift and compress the range of the initial pixel grey-level values in a region of interest (ROI) of a digitized mammographic image, according to grey-level analysis in 200 ROIs of real circumscribed lesions from digitized mammographic images. Simulation addresses lesion image characteristics, such as elliptical shape, orientation, halo sign for radiopaque lesions and capsule for radiolucent lesions, and is implemented in a user-driven PC-based interactive application. The appearance of the lesions is evaluated by six radiologists on a sample of 60 real and 60 simulated radiopaque lesions with the use of receiver operating characteristic (ROC) analysis. The area under the ROC curve, pooling the responses of the observers, was 0.55+/-0.03 indicating no statistically significant difference between real and simulated lesions (p>0.05). The method adequately simulates the mammographic appearance of circumscribed lesions and could be used to generate circumscribed lesion data sets for performance evaluation of image processing techniques, as well as education purposes.
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, 26500 Patras, Greece.
authors.forename: S
authors.initials: S
authors.lastname: Skiadopoulos
authors.forename: L
authors.initials: L
authors.lastname: Costaridou
authors.forename: C P
authors.initials: CP
authors.lastname: Kalogeropoulou
authors.forename: E
authors.initials: E
authors.lastname: Likaki
authors.forename: L
authors.initials: L
authors.lastname: Livos
authors.forename: G
authors.initials: G
authors.lastname: Panayiotakis
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging / Q000473:pathology; D001940:Breast / Q000473:pathology; D001941:Breast Diseases / Q000145:classification / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008327:Mammography; D008962:Models, Theoretical; D009558:Nipples / Q000000981:diagnostic imaging* / Q000473:pathology*; D015588:Observer Variation; D012372:ROC Curve; D016387:Women's Health