Computer-Aided Detection of Lesions in Digital Breast Tomosynthesis Images

The most common cancer among women in the western world is breast cancer. Early detection of lesions greatly influences the progress and success of its treatment. Digital breast tomosynthesis (DBT) is a new imaging technique that facilitates a three-dimensional reconstruction of the breast. DBT reduces superimposition of breast tissues and provides better insight into the breast compared to the common digital mammography. In order to assist radiologists with the examination and assessment of the large amount of DBT data, a computer aided detection (CADe) of focal lesions can be an essential tool, leading to increased sensitivity and specificity. We present and compare two different approaches for a fully automated detection of lesions in DBT data using voxel-wise classification, one being the state of the art and the other one an enhancement. Multiple difference of Gaussians detect lesions based on their common higher intensity and contrast in relation to surrounding tissue. A gradient orientation analysis detects round and spiculated lesions, even when they are weak in contrast and intensity. By combining these features and using a support vector machine, a classification performance of 88% can be achieved.

Authors and Affiliations

1. Department of Image Processing and Medical Engineering, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Deutschland

   Martin Prinzen, Florian Wagner, Sebastian Nowack & Thomas Wittenberg

2. Institute of Diagnostic Radiology, Erlangen University Hospital, Erlangen, Deutschland

   Rüdiger Schulz-Wendtland

3. Institute for Computational Visualistics, University of Koblenz-Landau, Koblenz-Landau, Deutschland

   Dietrich Paulus

Corresponding author

Correspondence to Martin Prinzen .

Editors and Affiliations

1. Institut für Medizinische Informatik, Uniklinik RWTH Aachen, Aachen, Germany

   Thomas Martin Deserno

2. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany

   Heinz Handels

3. Abteilung für Medizinische und Biologische Informatik, Deutsches Krebsforschungszentrum, Heidelberg, Germany

   Hans-Peter Meinzer

4. Institut für Medizinische Informatik, Charité – Universitätsmedizin Berlin, Berlin, Berlin, Germany

   Thomas Tolxdorff

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