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Segmentation of Cervical Cell Cluster by Multiscale Graph Cut Algorithm

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Business Intelligence and Information Technology (BIIT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 107))

Abstract

The segmentation and recognition of cervical cell clusters is a major challenge for the automatic screening of cervical cancer cells. This paper presents a model based on a multiscale graph cut algorithm for automatically segmenting cervical cell clusters. Global seed nodes are obtained using a multiscale graph cut algorithm to coarsely segment the cervical cell sample image and combine the confidence region method. Then, according to the global seed nodes and the global graph cut algorithm, the segmentation of the sample image is performed. The experimental data shows that the proposed algorithm is better than the currently widely used threshold watershed algorithm in DSC, accuracy, and recall measures.

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Acknowledgment

This work was supported by the project of talented youth reserves funded by the Harbin University of Commerce.

Author information

Authors and Affiliations

  1. Network and Education Technology Center, Harbin University of Commerce, Harbin, 150028, China

    Tao Wang

Authors
  1. Tao Wang
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Corresponding author

Correspondence to Tao Wang .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Yaoqun Xu

  3. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Zhijie Zhao

  4. Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada

    Sabah Mohammed

  5. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Zhipeng Fan

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Wang, T. (2022). Segmentation of Cervical Cell Cluster by Multiscale Graph Cut Algorithm. In: Hassanien, A.E., Xu, Y., Zhao, Z., Mohammed, S., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-030-92632-8_13

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