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

A Karahaliou, MSC 1 S Skiadopoulos, PHD 1 I Boniatis, MSC 1 P Sakellaropoulos, PHD 1 E Likaki, MD 2 G Panayiotakis, PHD 1 and L Costaridou, PHD 1

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


Figure 1
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  		Figure 1. Distribution of the case sample with respect to malignancy rating provided in the DDSM: 1, negative; 2, benign; 3, probably benign; 4, suspicious abnormality; and 5, highly suggestive of malignancy.

 

Figure 2
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  		Figure 2. (a) 600 x 600 pixel region of interest (ROI) containing a microcalcification (MC) cluster in the original mammogram (DDSM: volume cancer_09, case B_3406, RIGHT_CC). (b) Processed ROI with delineated MC cluster area. (c) Surrounding tissue ROI (ST-ROI). (d) Magnified 128 x 128 pixel subregion of ST-ROI.

 

Figure 3
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  		Figure 3. Receiver operating characteristic(ROC) curve for the best feature set of each textural feature category studied (first order statistics (FOS), grey level co-occurrence matrices (GLCMs), grey level run length matrices (GLRLMs) and Laws texture energy measures (LTEMs)).

 

Figure 4
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  		Figure 4. Receiver operating characteristic(ROC) curves corresponding to the classification scheme that combined the outputs of the three best textural feature sets (first order statistics (FOS), grey level co-occurrence matrices (GLCMs) and Laws texture energy measures (LTEMs)) and the DDSM assessment of malignancy. ROC curves' raw data points corresponding to the combined scheme ({blacksquare}) and the DDSM assessment ({blacktriangleup}) are also depicted.

 




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