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Diagnosis of breast tissue in mammography images based local feature descriptors

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Abstract

Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagnosis systems have been developed to improve diagnostic accuracy by radiologists. This work proposes a method for discriminating patterns of malignancy and benignity of masses in digitized mammography images through the analysis of local features. The method comparatively applies the Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB) and Local Binary Pattern (LBP) descriptors for local feature extraction. These features are represented by a Bag of Features (BoF) model, applied to provide new representations of the data and to reduce its dimensionality. Finally, the features are used as input for the Support Vector Machine (SVM), Adaptive Boosting (Adaboost) and Random Forests (RF) classifiers to differentiate malignant and benign masses. The method obtained significant results, reaching 100% sensitivity, 99.65% accuracy and 99.24% specificity for benign and malignant mass classification.

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Acknowledgments

The authors thanks CNPq and FAPEMA for the financial support.

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Authors and Affiliations

  1. Computer Applied Group, Federal University of Maranhão, São Luís, Maranhão, Brazil

    Caio Eduardo Falcão Matos, Johnatan Carvalho Souza, João Otávio Bandeira Diniz, Geraldo Braz Junior, Anselmo Cardoso de Paiva, João Dallyson Sousa de Almeida, Simara Vieira da Rocha & Aristófanes Correa Silva

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  1. Caio Eduardo Falcão Matos
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Matos, C.E.F., Souza, J.C., Diniz, J.O.B. et al. Diagnosis of breast tissue in mammography images based local feature descriptors. Multimed Tools Appl 78, 12961–12986 (2019). https://doi.org/10.1007/s11042-018-6390-x

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