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Classification of breast masses in mammograms using geometric and topological feature maps and shape distribution

Abstract

Purpose

Breast cancer is the second most common cancer in the world, being more common among women and representing 24.2% of new cases each year. Mammography is currently the best technique for early detection of non-palpable breast lesions. Due to the need to create new more computationally efficient techniques, this paper presents a methodology for mass classification from mammographic images based on their geometric and topological features.

Methods

For each image, two spatial feature maps named distance map and surface map are computed. These features describe the mass geometry and topology, respectively. Also, shape descriptors based on distances histograms are used to characterize the shape of the masses. The purpose of this comparison is to discriminate its malignancy and benignity patterns. The high-boost filter is applied to enhance the masses, since the difference between them and the breast tissue or other components of them is very subtle. Mammograms digitized from the Digital Database for Screening Mammography (DDSM) were used for the testing of this methodology, corresponding to 794 ROIs that were separated into groups by density, according to BI-RADS classification.

Results

The best results for accuracy, sensitivity, and specificity were 93.70%, 96.29%, and 91.05%, respectively, for density 2 and 90.18%, 91.01%, and 89.94% for all images.

Conclusion

The results obtained demonstrate that the sets of features successfully discriminate mass standards, even with the exceptions and obstacles that characterize and classify the masses through their shape.

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Acknowledgments

The authors acknowledge Applied Computing Center (NCA), Federal University of Maranhão (UFMA), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão (FAPEMA), and, finally, the Massachusetts General Hospital and laboratories Sandia National from University of South Florida by the public available DDSM database used in this study.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support.

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Correspondence to Thamila Fontenele de Brito Silva.

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de Brito Silva, T.F., de Paiva, A.C., Silva, A.C. et al. Classification of breast masses in mammograms using geometric and topological feature maps and shape distribution. Res. Biomed. Eng. 36, 225–235 (2020). https://doi.org/10.1007/s42600-020-00063-x

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  • DOI: https://doi.org/10.1007/s42600-020-00063-x

Keywords

  • Breast cancer
  • High-boost filter
  • Spatial maps
  • Shape distribution