title: Improvement of computerized mass detection on mammograms: fusion of two-view information.
abstract: Recent clinical studies have proved that computer-aided diagnosis (CAD) systems are helpful for improving lesion detection by radiologists in mammography. However, these systems would be more useful if the false-positive rate is reduced. Current CAD systems generally detect and characterize suspicious abnormal structures in individual mammographic images. Clinical experiences by radiologists indicate that screening with two mammographic views improves the detection accuracy of abnormalities in the breast. It is expected that the fusion of information from different mammographic views will improve the performance of CAD systems. We are developing a two-view matching method that utilizes the geometric locations, and morphological and textural features to correlate objects detected in two different views using a prescreening program. First, a geometrical model is used to predict the search region for an object in a second view from its location in the first view. The distance between the object and the nipple is used to define the search area. After pairing the objects in two views, textural and morphological characteristics of the paired objects are merged and similarity measures are defined. Linear discriminant analysis is then employed to classify each object pair as a true or false mass pair. The resulting object correspondence score is combined with its one-view detection score using a fusion scheme. The fusion information was found to improve the lesion detectability and reduce the number of FPs. In a preliminary study, we used a data set of 169 pairs of cranio-caudal (CC) and mediolateral oblique (MLO) view mammograms. For the detection of malignant masses on current mammograms, the film-based detection sensitivity was found to improve from 62% with a one-view detection scheme to 73% with the new two-view scheme, at a false-positive rate of 1 FP/image. The corresponding cased-based detection sensitivity improved from 77% to 91%.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor 48109-0030, USA. chanhp@umich.edu
authors.forename: Sophie
authors.initials: S
authors.lastname: Paquerault
authors.forename: Nicholas
authors.initials: N
authors.lastname: Petrick
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Mark A
authors.initials: MA
authors.lastname: Helvie
mesh_terms: D000465:Algorithms; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography / Q000295:instrumentation* / Q000379:methods*; D012984:Software