Course Content

Mammography is a widely used diagnostic technique for early breast cancer detection in women. Clusters of Microcalcification are the sign of breast cancer and their detection will decrease the probability of mortality rate and improves its prognosis. The detection of microcalcification clusters is a difficult task for radiologists because of variations of size and orientation and are highly correlated with background tissue. In this paper, we present a Computer Aided Detection (CAD) method, which is used to detect nodules (microcalcification) in mammograms. We have designed a multi-scale filter bank based on the concept of second-order partial derivatives (Hessian matrix). Regions Of Interest (ROI) are identified by a multiresolution based histogram technique. This ROI of mammogram is decomposed into sub-bands, the low-frequency subband is suppressed and then the high-frequency subbands which contain only nodule-like structures are reconstructed. This structure is determined by the eigenvalues of the Hessian matrix. The detection performance of the proposed method is evaluated by comparing our results with two traditional wavelet based methods. Experimental results show that the microcalcifications can be efficiently detected by proposed method and it has high true positive ratio in comparison to other methods.