Segmentation of suspicious clustered microcalcifications in mammograms.

We have developed a multistage computer-aided diagnosis (CAD) scheme for the automated segmentation of suspicious microcalcification clusters in digital mammograms. The scheme consisted of three main processing steps. First, the breast region was segmented and its high-frequency content was enhanced using unsharp masking. In the second step, individual microcalcifications were segmented using local histogram analysis on overlapping subimages. For this step, eight histogram features were extracted for each subimage and were used as input to a fuzzy rule-based classifier that identified subimages containing microcalcifications and assigned the appropriate thresholds to segment any microcalcifications within them. The final step clustered the segmented microcalcifications and extracted the following features for each cluster: the number of microcalcifications, the average distance between microcalcifications, and the average number of times pixels in the cluster were segmented in the second step. Fuzzy logic rules incorporating the cluster features were designed to remove nonsuspicious clusters, defined as those with typically benign characteristics. A database of 98 images, with 48 images containing one or more microcalcification clusters, provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The results showed a true positive rate of 93.2% and an average of 0.73 false positive clusters per image. A comparison of our results with other reported segmentation results on the same database showed comparable sensitivity and at the same time an improved false positive rate. The performance of the CAD scheme is encouraging for its use as an automatic tool for efficient and accurate diagnosis of breast cancer.

Simulation model of mammographic calcifications based on the American College of Radiology Breast Imaging Reporting and Data System, or BIRADS.

RATIONALE AND OBJECTIVES: The authors developed and evaluated a method for the simulation of calcification clusters based on the guidelines of the Breast Imaging Reporting and Data System of the American College of Radiology. They aimed to reproduce accurately the relative and absolute size, shape, location, number, and intensity of real calcifications associated with both benign and malignant disease. MATERIALS AND METHODS: Thirty calcification clusters were simulated by using the proposed model and were superimposed on real, negative mammograms digitized at 30 microns and 16 bits per pixel. The accuracy of the simulation was evaluated by three radiologists in a blinded study. RESULTS: No statistically significant difference was observed in the observers' evaluation of the simulated clusters and the real clusters. The observers' classification of the cluster types seemed to be a good approximation of the intended types from the simulation design. CONCLUSION: This model can provide simulated calcification clusters with well-defined morphologic, distributional, and contrast characteristics for a variety of applications in digital mammography.