A Texture Analysis Approach for Characterizing Microcalcifications on Mammograms

The current study investigates whether texture properties of the tissue surrounding microcalcification (MC) clusters can contribute to breast cancer diagnosis. The case sample analyzed consists of 100 mammographic images, originating from the Digital Database for Screening Mammography (DDSM). All mammograms selected correspond to heterogeneously and extremely dense breast parenchyma and contain subtle MC clusters (46 benign and 54 malignant, according to database ground truth tables). Regions of interest (ROIs) of 128x128 pixels, containing the MCs are used for the subsequent texture analysis. ROIs are preprocessed using a wavelet-based locally adapted contrast enhancement method and a thresholding technique is applied to exclude MCs. Texture features are extracted from the remaining ROI area (surrounding tissue) employing first and second order statistics algorithms, grey level run length matrices and Laws’ texture energy measures. Differentiation between malignant and benign MCs is performed using a knearest neighbour (kNN) classifier and employing the leaveone-out training-testing methodology. The Laws’ texture energy measures demonstrated the highest performance achieving an overall accuracy of 89%, sensitivity 90.74% (49 of 54 malignant cases classified correctly) and specificity 86.96% (40 of the 46 benign cases classified correctly). Texture analysis of the tissue surrounding MCs shows promising results in computer-aided diagnosis of breast cancer and may contribute to the reduction of benign biopsies.