Background: Computer Aided Diagnosis (CADx) with screening mammography could
help radiologists and doctors in reliable and accurate identification of breast cancer at the preliminary
Objective: To propose an early diagnosis technique using fusion of texture features extracted from
both Medio Lateral Oblique (MLO) and Cranio Caudal (CC) view mammograms.
Methods: The proposed two-view CADx system segments the tumour region by fuzzy c-means
clustering. The texture features extracted from MLO and CC view are reduced by Principal Component
Analysis (PCA) or Canonical Correlation Analysis (CCA) and fused serially or parallelly
followed by support vector machine classifier (SVM).
Results: An improvement in accuracy of 4.4% and 7.05% was achieved with serial fusion using
CCA for DDSM and INbreast datasets respectively. It is also observed that there is a significant
improvement in specificity, F1 measure, kappa coefficient and Balanced Classification Rate (BCR)
with serial fusion using CCA for all four texture features irrespective of the datasets. A significant
improvement in BCR of 3.26% and 7.52% was achieved with LAWs feature for DDSM and INbreast
Conclusion: LAWs provide almost all types of texture variations such as edges, ripples and spots.
CCA transforms the feature vectors in such a way that the transformed features have maximum
cross correlation and minimum auto correlation. Hence, it provides more relevant features and
consequently improves the performance. Thus, our method could be used to assist doctors in enhancing
the effectiveness of breast cancer diagnosis and start the treatment in earlier stage of the