Comparison of methods for classification of breast ductal branching patterns

Authors:
Predrag R. Bakic;Despina Kontos;Vasileios Megalooikonomou;Mark A. Rosen;Andrew D. A. Maidment
Affiliations:
Department of Radiology, University of Pennsylvania, Philadelphia, PA;Computer and Information Sciences Department, Temple University, Philadelphia, PA;Computer and Information Sciences Department, Temple University, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Year:
2006

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Abstract

Topological properties of the breast ductal network have shown the potential for classifying clinical breast images with and without radiological findings. In this paper, we review three methods for the description and classification of breast ductal topology. The methods are based on ramification matrices and symbolic representation via string encoding signatures. The performance of these methods has been compared using clinical x-ray and MR images of breast ductal networks. We observed the accuracy of the classification between the ductal trees segmented from the x-ray galactograms with radiological findings and normal cases in the range of 0.86-0.91%. The accuracy of the classification of the ductal trees segmented from the MR autogalactograms was observed in the range of 0.5-0.89%.