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Morph_SPCNN model and its application in breast density segmentation

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Abstract

Breast density is known as a significant indicator of breast cancer risk prediction and greatly reduces the digital mammograms sensitivity. In this work, based on the simple pulse coupled neural network (SPCNN), a novel Morph_SPCNN model is proposed for dealing with the limitations of over-segmentation that commonly existed in density segmentation of mammograms. To evaluate the proposed model, the segmentation result is employed as a feature map of the breast density classification system. In addtion, the texture features of mammogram calculated based on the gray level co-occurrence matrix (GLCM) and the statistical features (mean, skewness, kurtosis) are extracted and input to the support vector machine (SVM) for breast density classification. Finally, the performance of SVM classifier is evaluated based on the ten-fold cross-validation. Our method is verified both on the MIAS dataset, DDSM database and hybrid dataset (MIAS database and Gansu Provincial Academy of Medical Sciences (GPAMS) database), respectively achieving 87.80%, 94.89% and 95.37% accuracy for breast density classification. The experimental results indicate that our proposed method has greatly improved the performance of breast density segmentation and classification.

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Acknowledgement

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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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China

    Yunliang Qi & Yide Ma

  2. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070, China

    Zhen Yang & Jing Lian

  3. The First Hospital of Lanzhou University, Lanzhou, Gansu, 730000, China

    Junqiang Lei

  4. The School of Data Science and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Jizhao Liu

  5. First school of clinical medicine, Lanzhou University, Lanzhou, Gansu, 730000, China

    Wen Feng

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  1. Yunliang Qi
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Qi, Y., Yang, Z., Lei, J. et al. Morph_SPCNN model and its application in breast density segmentation. Multimed Tools Appl 80, 2821–2845 (2021). https://doi.org/10.1007/s11042-020-09796-4

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