title: Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation.
abstract: A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve  (A(z))  was 0.93, which was significantly higher than the performance without spiculation features  (A(z)=0.90,  p=0.02). On a subset of 88 cases, classification performance of CAD  (A(z)=0.90)  was comparable to the average performance of 10 readers  (A(z)=0.87).
authors.affiliation: Department of Radiology, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands. t.tan@rad.umcn.nl
authors.forename: Tao
authors.initials: T
authors.lastname: Tan
authors.forename: Bram
authors.initials: B
authors.lastname: Platel
authors.forename: Henkjan
authors.initials: H
authors.lastname: Huisman
authors.forename: Clara I
authors.initials: CI
authors.lastname: Sánchez
authors.forename: Roel
authors.initials: R
authors.lastname: Mus
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D012372:ROC Curve; D016217:Ultrasonography, Mammary / Q000379:methods*