Towards localization of malignant sites of asymmetry across bilateral mammograms. (March 2017)

• Record Type: Journal Article
• Authors: Casti, P.; Mencattini, A.; Salmeri, M.; Ancona, A.; Lorusso, M.; Pepe, M.L.; Natale, C. Di; Martinelli, E.
• Is Part Of: Computer methods and programs in biomedicine. Volume 140(2017)
• Page Start: 11
• Page End: 18
• Publication Date: 2017-03
• Subjects: Bilateral asymmetry -- Breast cancer -- Correlation-based structural similarity -- Computer-aided diagnosis -- Detection of malignant sites; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.11.010
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Localization of malignant sites of asymmetry in mammograms. Combination of Tabar masking procedures. Use of correlation-based structural similarity descriptors. Database-independent validation strategy. Abstract: Background and objectives: The analysis of patterns of asymmetry between the left and right mammograms of a patient can provide meaningful insights into the presence of an underlying tumor in its early stage. However, the identification of breast cancer by investigating bilateral asymmetry is difficult to perform due to the indistinct and borderline nature of the asymmetric signs as they appear on mammograms. Methods: In this study, to increase the positive-predictive value of asymmetry in mammographic screening, a novel computerized approach for the automatic localization of malignant sites of asymmetry in mammograms is proposed. The sites of anatomical correspondence between the right and left regions of each radiographic projection were extracted by means of two bilateral masking procedures, inspired by radiologists' criteria in interpreting mammograms and based on the use of detected landmarking structures. Relative variations of spatial patterns of intensity values and of orientations of directional components within each site were quantified by combining multidirectional Gabor filters and indices of structural similarity. The localization of the sites of malignant asymmetry was performed by coupling two quadratic discriminant analysis classifiers, one for each masking procedure, that assigned the likelihood of malignancy to each site of correspondence. Results: The performance of the proposed method was assessed on 94 mammographic images from two publicly available databases and containing at least one asymmetric site. Sensitivity, specificity and balanced accuracy levels of 0.83 (0.09), 0.75 (0.06), and 0.79 (0.04), respectively were obtained in the classification of malignant asymmetric sites vs benign/normal sites using cross-validation. In addition, a further blind test on a dataset of Full Field Digital Mammograms achieved levels of sensitivity, specificity, and balanced accuracy of 0.86, 0.65, and 0.75, respectively. Conclusions: The achieved performance indicates that the proposed system is effective in localizing sites of malignant asymmetry and it is expected to improve computer-aided diagnosis of breast cancer.