Detection of clustered microcalcifications in masses on mammograms by artificial neural networks
Xuejun Zhang, Takeshi Hara, Hiroshi Fujita, Takuji Iwase, Tokiko Endo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
The existence of a cluster of microcalcifications in mass area on mammogram is one of important features for distinguishing the breast cancer between benign and malignant. However, missed detections often occur because of its low subject contrast in denser background and small quantity of microcalcifications. To get a higher performance of detecting the cluster in mass area, we combined the shift-invariant artificial neural network (SIANN) with triple-ring filter (TRF) method in our computer-aided diagnosis (CAD) system. 150 region-of- interests around mass containing both of positive and negative microcalcifications were selected for training the network by a modified error-back-propagation algorithm. A variable-ring filter was used for eliminating the false- positive (FP) detections after the outputs of SIANN and TRF. The remained Fps were then reduced by a conventional three layer artificial neural network. Finally, the program identified clustered microcalcifications form individual microcalcifications. In a practical detection of 30 cases with 40 clusters in masses, the sensitivity of detecting clusters was improved form 90% by our previous method to 95% by using both SIANN and TRF, while the number of FP clusters was decreased from 0.85 to 0.40 cluster per image.