Automatic shape analysis and classification of mammographic calcifications

The morphology and distribution of mammographic calcifications and the way these elements vary within a cluster are valuable in distinguishing between benign and malignant calcifications. The specific aims of this study were (a) the development of an automatic tool that differentiates between benign and malignant clustered calcifications based on their morphological properties and (b) the detennination of the effects of image spatial resolution on the classification process. The long term aim of the project is to use this tool to categorize detected clusters into the various types described in the Breast Imaging Reporting and Data System (BIRADS) of the American College of Radiology (ACR) and assist the radiologists in their diagnosis. Shape analysis was used in the design of our computer scheme. Six shape factors (area, compactness, moments, Fourier descriptors, eccentricity, and spread) were selected as the most representative of the morphological properties of the mammographic calcifications and were measured for each calcification in the test set. Feature vectors with elements the mean values and standard deviations of the six shape features were calculated for each cluster in the test set. The feature vectors were used as inputs to an artificial neural network which classified the clusters as benign or malignant. The performance of the classifier was evaluated with the leave-one-out method. The test set consisted of 30 simulated calcification clusters (15 benign and 15 malignant), 13 biopsy radiographs with calcifications (11 benign and 2 malignant), and 8 mammograms with real calcifications (2 benign and 6 malignant). Simulation was based on the benign/malignant calcification types described in the ACR BIRADS. The simulated clusters were embedded on real, negative mammograms digitized at a resolution of 30 μm and 16 bits per pixel. The biopsy radiographs and the mammograms with the real calcification clusters were also digitized at 30 μm and 16 bits per pixel. The image pixel size was then mathematically reduced to 60, and 90 μm. Hence, a total of 153 images with same number of clusters was used for testing. An expert manually outlined each calcification in these clusters (both simulated and real). Shape factors were measured for the original simulated calcifications as well as all manually segmented clusters. The results showed that shape analysis can provide robust features for the classification of mammographic calcification clusters. Image spatial resolution affects the classification outcome, particularly in the cases of microcalcifications where shape and size information loss is more pronounced at lower resolution images. Hence, the classification accuracy decreased as the spatial resolution was reduced from 30 to 90 μm per pixel, independent of whether it was a simulated or real case. Manual segmentation of calcifications by an expert introduces errors that can be costly in the classification process. Robust features and a representative training set, as that provided by the simulation, can partly compensate for such errors. However, an automatic segmentation tool is necessary to further improve the proposed classification method. The developed computer scheme can assist in the differentiation of the various calcification types as well as provide an evaluation tool for CAD methodologies, such as segmentation and detection algorithms.