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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References
Casti P, Mencattini A, Salmeri M, Ancona A, Mangeri F, Pepe ML, et al. Contour-independent detection and classification of mammographic lesions. Biomed Signal Process Control. 2016;25:165–77.
Casti P, Mencattini A, Salmeri M, Rangayyan RM. Computerized analysis of mammographic images for detection and characterization of breast cancer. Synth Lect Biomed Eng. 2017;12(1):1–86.
Chakraborty J, Midya A, Mukhopadhyay S, Rangayyan RM, Sadhu A, Singla V, et al. Computer-aided detection of mammographic masses using hybrid region growing controlled by multilevel thresholding. J Med Biol Eng. 2019;39(3):352–66.
D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA, et al. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology [internet]. 2013 [cited 2018 Dec 06].
Elmoufidi A, El Fahssi K, Jai-Andaloussi S, Sekkaki A, Gwenole Q, Lamard M. Anomaly classification in digital mammography based on multiple-instance learning. IET Image Process. 2017;12(3):320–8.
Firmino M, Angelo G, Morais H, Dantas MR, Valentim R. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online. 2016;15(1):2.
Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP. The digital database for screening mammography [internet]. University of South Florida. Tampa, USA; 2000; cited [2018 May 16].
International Agency for Research on Cancer. IARC. Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. 2018: 263:1–3.
Luna-Benoso B, Martínez JC, Carapia RF, Garcia VMS. Identification of abnormalities in mammograms images using methods in the spatial domain. Appl Math Sci. 2013;7(134):6695–704.
Osada R, Funkhouser T, Chazelle B, Dobkin D. Shape distributions. ACM Trans Graphics. 2002;21(4):807–32.
Rangayyan RM, Nguyen TM. Fractal analysis of contours of breast masses in mammograms. J Digit Imaging. 2007;20(3):223–37.
Rangayyan RM, Banik S, Desautels JL. Detection of architectural distortion in prior mammograms via analysis of oriented patterns. J Vis Exp. 2013;78:e50341.
Rocha SV, Junior GB, Silva AC, Paiva AC, Gattass M. Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution. Expert Syst Appl. 2016;66:7–19.
Souza JC, Silva TF, Rocha SV, Paiva AC, Braz G, Almeida JD, Silva AC (2018) Classification of malignant and benign tissues in mammography using dental shape descriptors and shape distribution
Sun L, Wang J, Hu Z, Xu Y, Cui Z. Multi-view convolutional neural networks for mammographic image classification. IEEE Access. 2019;7:126273–82.
Tai SC, Chen ZS, Tsai WT. An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Informa. 2013;18(2):618–27.
Yu M, Atmosukarto I, Leow WK, Huang Z, Xu R. 3D model retrieval with morphing based geometric and topologic topological feature maps. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2003 Jun 16–22; Madison, Wisconsin. USA: IEEE; 2003.p. II-656.
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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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