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Breast density analysis based on glandular tissue segmentation and mixed feature extraction

  • Xiaonan Gong
  • Zhen Yang
  • Deyuan Wang
  • Yunliang Qi
  • Yanan Guo
  • Yide MaEmail author
Article

Abstract

Breast cancer poses a threat to the lives of many women. Breast density is a closely related indicator of breast cancer risk. The aim of this paper is to propose a classification system for breast density, which can appropriately segment the glandular tissue from the whole breast and to achieve a better classification result. A new threshold method is applied to segment the breast glandular tissue. The gray level co-occurrence matrix (GLCM) is implemented to extract the texture features of the glandular tissue. Meanwhile, we obtain three statistical features (mean, skewness, kurtosis). In addition, the calculated breast density that is served as a new feature is added to the feature vectors. The mixed feature vectors are classified by Support Vector Machine (SVM) and Ultimate Learning Machine (ELM). Ten-fold cross-validation is used to verify the classifier performance. The system using the SVM achieves 96.19% accuracy for three density types in the MIAS database and achieves 96.35% accuracy of four density types in the DDSM database. The accuracy in the database mixed with the local database was 95.01% and there are three density types in the mixed database. The experimental results indicate that the system proposed has a better performance in breast density classification. The system proposed in this paper can be considered to help the physician to classify breast density.

Keywords

Breast cancer Breast density Threshold segmentation Feature extraction SVM 

Nomenclature

DDSM

Digital database for screening mammography

MIAS

Mammographic image analysis society

CAD

Computer-aided diagnosis

ROI

Region of interest

SWB

Segmented whole breast

SGT

Segmented glandular tissue

BI-RADS

Breast imaging-reporting and data system

GLCM

Gray level co-occurrence matrix

Notes

Acknowledgements

This work is jointly supported by the Natural Science Foundation of Gansu Province (No.18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No.lzujbky-2017-it72 and No.lzujbky-2018-it61).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xiaonan Gong
    • 1
  • Zhen Yang
    • 1
  • Deyuan Wang
    • 1
  • Yunliang Qi
    • 1
  • Yanan Guo
    • 1
  • Yide Ma
    • 1
    Email author
  1. 1.School of Information Science EngineeringLanzhou UniversityLanzhouChina

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