Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach

Erick Rodríguez-Esparza; Laura A. Zanella-Calzada; Diego Oliva; Marco Pérez-Cisneros

doi:10.1117/12.2549899

16 March 2020 Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach

Erick Rodríguez-Esparza, Laura A. Zanella-Calzada, Diego Oliva, Marco Pérez-Cisneros

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131424 (2020) https://doi.org/10.1117/12.2549899
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Breast cancer represents the most common type of cancer worldwide among women. One of the most important diagnostic methods of this disease are mammograms, however, the high prevalence of breast cancer has not been reduced due to the incorrect diagnosis of these images, since they can be complex to interpret. An approach that represents a fundamental process for the improvement of this diagnosis is digital image processing, since it can facilitate the interpretation of the images for the specialists. In this work is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, identifying regions within the breast that have abnormal tissue. Then, these regions are subjected to an automatic classification system based on a bag-of-visual-words (BoVW) approach to identify healthy tissue, benign tumors, and malignant tumors. According to the results, the classifier reached an average accuracy of 0.86 in the training stage and 0.73 in the testing, proving to be statistically significant in the automatic classification of mammograms, presenting a preliminary tool for the support of specialists in the diagnosis of mammography images.

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Erick Rodríguez-Esparza, Laura A. Zanella-Calzada, Diego Oliva, and Marco Pérez-Cisneros "Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131424 (16 March 2020); https://doi.org/10.1117/12.2549899

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