title: Characterizing Architectural Distortion in Mammograms by Linear Saliency.
abstract: Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (Az) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.
authors.affiliation: Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia.
authors.forename: Fabián
authors.initials: F
authors.lastname: Narváez
authors.affiliation: Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia.
authors.forename: Jorge
authors.initials: J
authors.lastname: Alvarez
authors.affiliation: Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia.
authors.forename: Juan D
authors.initials: JD
authors.lastname: Garcia-Arteaga
authors.affiliation: Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Tarquino
authors.affiliation: Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia. edromero@unal.edu.co.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Romero
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008327:Mammography / Q000379:methods*; D008390:Markov Chains; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Architectural distortion; Breast spiculated lesions; Linear saliency; Mammography