Classification of linear structures in mammograms using random forests

Authors:
Zezhi Chen;Michael Berks;Susan Astley;Chris Taylor
Affiliations:
Imaging Science and Biomedical Engineeering, School of Cancer and Enabling Sciences, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineeering, School of Cancer and Enabling Sciences, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineeering, School of Cancer and Enabling Sciences, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineeering, School of Cancer and Enabling Sciences, University of Manchester, Manchester, UK
Venue:
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Year:
2010

Citing 2
Cited 1

Random Forests

Machine Learning
Statistical Modelling of Lines and Structures in Mammograms

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Detecting and classifying linear structures in mammograms using random forests

IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging

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Abstract

Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic.