A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry

This study aims to develop and test a new computer-aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities.

The proposed scheme is based on the analysis of both local and global bilateral mammographic feature asymmetries. The level of local or global asymmetry is assessed by analyzing mammographic features extracted from the bilaterally matched regions of interest (ROIs), or from the entire breast, respectively. The selected local and global feature vectors are combined and classified using a maximum likelihood obtained from a naïve Bayes classifier. This scheme was evaluated using a leave-one-case-out cross-validation method that was applied to 243 subjects from mini-MIAS and INbreast databases. In addition, the result is compared with a conventional unilateral (or single) image-based CAD scheme.

Using a case-based evaluation approach and an area under curve (AUC) of the receiver operating characteristic (ROC) as a performance index, the new scheme yielded AUC = 0.79 ± 0.07, an 8.2% increase compared with AUC = 0.73 ± 0.08 obtained using the unilateral image-based CAD scheme.

This work demonstrates that applying bilateral asymmetry analysis increases the discriminatory power of CAD schemes while optimizing the likelihood assessment of breast abnormalities presence. Therefore, the proposed CAD approach provides the radiologist with beneficial supplementary information and can indicate high-risk cases.