Fusion of two view binary patterns to improve the performance of breast cancer diagnosis
S. Sasikala, M. Ezhilarasi · 2017
Breast cancer remains the leading cause of cancer deaths among women globally. To reduce the breast cancer mortality, early detection, diagnosis and treatment is an important requirement. Computer Aided Diagnosis (CADx) techniques with screening mammography is widely used for this purpose. Further, improvements in CAD systems were achieved by using both Medio Lateral Oblique (MLO) and Cranio Caudal (CC) view mammograms. In this study, fusion of Local Binary Patterns of MLO and CC view images using Canonical Correlation Analysis (CCA) is proposed to improve the diagnostic accuracy and to reduce the false positive rate of the two view systems. Two data bases, Digital Database for Screening Mammography (DDSM) and INbreast are used to evaluate the performance of the proposed system. A significant improvement in the performance of the double view system is obtained with CCA. The accuracy of 96.1% and 95.3% were obtained for DDSM and INbreast dataset respectively with serial fusion of LBP features. The proposed system could help the radiologists in diagnosis so that treatment can be started earlier during the disease and therefore reduce the mortality.