Classification of ductal tree structures in galactograms

Authors:
Angeliki Skoura;Michael Barnathan;Vasileios Megalooikonomou
Affiliations:
Computer Engineering and Informatics Department, University of Patras, Patras, Greece;Data Engineering Lab, Center for Information Science and Technology, Temple University, Philadelphia;Computer Engineering and Informatics Department, University of Patras, Patras, Greece and Data Engineering Lab, Center for Information Science and Technology, Temple University, Philadelphia
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

The objective of this study is the classification of galactograms, medical images which depict the ductal tree of human breast, in order to provide insight into the relationship between tree topology and radiological findings regarding breast cancer. We present two different descriptors for the classification of the ductual trees; the tree asymmetry index and the maximum common skelton, both of which quantify the similarity between tree topologies. Experimental results demonstrate the effectiveness of the proposed approach, which reaches a classification accuracy of 83%, and also indicate that our method can potentially aid in early breast cancer diagnosis.