title: An evaluation and comparison of the performance of state of the art approaches for the detection of spiculated masses in mammograms.
abstract: Mammography is the standard examination method for the early detection of breast cancer. In the last decade, computer assisted detection systems have been developed that assist the physician in the detection of suspicious regions in mammograms. However, recent clinical studies indicate that state of the art CAD systems might have a negative impact on the accuracy of screening mammography. Therefore, besides additional clinical studies, better evaluations of state of the art detection approaches are necessary. In this contribution three methods for the detection of spiculated masses in mammograms are evaluated and compared. All three of them are based on gradient orientation images. To detect masses, the methods use circular neighbourhoods with different sizes around a single pixel. The number of orientations in every neighbourhood is used by every method in different ways to form a result. The main contribution is the first fair comparison of the performance of different detection approaches for spiculated masses. Furthermore, a novel gradient direction analysis is introduced. The analysis is an extension to the three approaches, which increases the performance for one of the three approaches.
authors.affiliation: Department of Image Processing, Medical Engineering at the Fraunhofer-Institute for Integrated Circuits (IIS), Erlangen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Kage
authors.forename: Matthias
authors.initials: M
authors.lastname: Elter
authors.forename: Thomas
authors.initials: T
authors.lastname: Wittenberg
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013673:Technology Assessment, Biomedical