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Bi-SCM: bidirectional spiking cortical model with adaptive unsharp masking for mammography image enhancement

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Abstract

Mammography technique is commonly used for diagnosing breast cancer, but mammography images usually show low contrast which cause difficulties to clinical diagnosis. Therefore, improving the visual quality of mammography images is an important issue. This is a challenging problem, because every mammography image consists of rich textures, including bright areas, dark areas, and textural details. Inspired by bio-inspired neural network, this paper proposes a Bidirectional Spiking Cortical Model (Bi-SCM) from the perspective of neural information fusion to enhance the contrast of bright areas and dark areas adequately, as well as textural details. This goal is achieved by utilizing the Bi-SCM to first enhance a mammography image and its inverse separately. The enhanced results are fused by a new fusion algorithm based on non-subsampled contourlet transform (NSCT) to ensure that both of the contrast of bright areas and dark areas are adequately improved. The textual details are then enhanced by an unsharp masking method which consists of cubic filter and log-ratio operation. Sufficient experiments on mammography images are conducted to evaluate the proposed approach. Experimental results show that the proposed method outperforms state-of-the-art methods on both enhancing contrast and details. Besides, over-enhancement and noise sensitivity are also significantly suppressed.

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Data Availability

The datasets analysed during the current study are available at https://www.mammoimage.org/databases/and http://www.eng.usf.edu/cvprg/Mammography/Database.html.

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under grant 62001110, the Natural Science Foundation of Jiangsu Province under grant BK20200353, andthe 111 Project under grant B17040.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Yaping Yan

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

    Hongjuan Zhang & Yide Ma

  3. School of Automation, Southeast University, Nanjing, 210096, China

    Songlin Du

  4. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Songlin Du

  5. Engineering Research Center of Intelligent Geodetection Technology, Ministry of Education, Wuhan, 430074, China

    Songlin Du

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  1. Yaping Yan
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Correspondence to Yaping Yan.

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Yan, Y., Zhang, H., Du, S. et al. Bi-SCM: bidirectional spiking cortical model with adaptive unsharp masking for mammography image enhancement. Multimed Tools Appl 82, 12081–12098 (2023). https://doi.org/10.1007/s11042-022-13766-3

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  • DOI: https://doi.org/10.1007/s11042-022-13766-3

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