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First published online July 9, 2007
British Journal of Radiology (2007) 80, 648-656
© 2007 British Institute of Radiology
doi: 10.1259/bjr/30415751
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Full paper

Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis

A Karahaliou, MSC1, S Skiadopoulos, PHD1, I Boniatis, MSC1, P Sakellaropoulos, PHD1, E Likaki, MD2, G Panayiotakis, PHD1 and L Costaridou, PHD1

Departments of 1 Medical Physics and 2 Department of Radiology, School of Medicine, University of Patras, 265 00 Patras, Greece

Correspondence: Dr Lena Costaridou, Department of Medical Physics, School of Medicine, University of Patras, Patras 26 500, Greece. E-mail: costarid{at}upatras.gr

Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether texture properties of the tissue surrounding MCs can contribute to breast cancer diagnosis. A case sample of 100 biopsy-proved MC clusters (46 benign, 54 malignant) from 85 dense mammographic images, included in the Digital Database for Screening Mammography, was analysed. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet-based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (surrounding tissue) was subjected to texture analysis. Four categories of textural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws' texture energy measures) were extracted from the surrounding tissue. The ability of each feature category in discriminating malignant from benign tissue was investigated using a k-nearest neighbour (kNN) classifier. An additional classification scheme was performed by combining classification outputs of three textural feature categories (the most discriminating ones) with a majority voting rule. Receiver operating characteristic (ROC) analysis was conducted for classifier performance evaluation of the individual textural feature categories and of the combined classification scheme. The best performance was achieved by the combined classification scheme yielding an area under the ROC curve (Az) of 0.96 (sensitivity 94.4%, specificity 80.0%). Texture analysis of tissue surrounding MCs shows promising results in computer-aided diagnosis of breast cancer and may contribute to the reduction of unnecessary biopsies.




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