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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: 16 Feb 202019 Feb 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period16.2.2019.2.20

Keywords

  • bag-of-visual-words.
  • breast tumor
  • classication
  • digital image processing
  • mammograms
  • mcet-hho algorithm

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach'. Together they form a unique fingerprint.

  • Cite this

    Rodríguez-Esparza, E., Zanella-Calzada, L. A., Oliva, D., & Pérez-Cisneros, M. (2020). Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [1131424] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2549899