title: Gabor filters and phase portraits for the detection of architectural distortion in mammograms.
abstract: Architectural distortion is a subtle abnormality in mammograms, and a source of overlooking errors by radiologists. Computer-aided diagnosis (CAD) techniques can improve the performance of radiologists in detecting masses and calcifications; however, most CAD systems have not been designed to detect architectural distortion. We present a new method to detect and localise architectural distortion by analysing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The curvilinear structures (CLS) of interest (spicules and fibrous tissue) are separated from confounding structures (pectoral muscle edge, parenchymal tissue edges, breast boundary, and noise). The selected core CLS pixels and the orientation field are filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analysed to produce three phase portrait maps: node, saddle, and spiral. The node map is further analysed in order to detect the sites of architectural distortion. The method was tested with 19 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 84% was obtained at 7.8 false positives per image.
authors.affiliation: Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada. ranga@ucalgary.ca
authors.forename: Rangaraj M
authors.initials: RM
authors.lastname: Rangayyan
authors.forename: Fábio J
authors.initials: FJ
authors.lastname: Ayres
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008433:Mathematics; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012680:Sensitivity and Specificity