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International Conference on Neural Information Processing

ICONIP 2015: Neural Information Processing pp 390–400Cite as

Learning Shape-Driven Segmentation Based on Neural Network and Sparse Reconstruction Toward Automated Cell Analysis of Cervical Smears

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

Abstract

The development of an automatic and accurate segmentation approach for both nuclei and cytoplasm remains an open problem due to the complexities of cell structures resulting from inconsistent staining, poor contrast, and the presence of mucus, blood, inflammatory cells, and highly overlapping cells. This paper introduces a computer vision slide analysis technique of two stages: the 3-class cellular component classification, and individual cytoplasm segmentation. Feed forward neural network along with discriminative shape and texture features is applied to classify the cervical cell images in the cellular components. Then, a learned shape prior incorporated with variational framework is applied for accurate localization and delineation of overlapping cells. The shape prior is dynamically modelled during the segmentation process as a weighted linear combination of shape templates from an over-complete shape repository. The proposed approach is evaluated and compared to the state-of-the-art methods on a dataset of synthetically generated overlapping cervical cell images, with competitive results in both nuclear and cytoplasmic segmentation accuracy.

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Author information

Authors and Affiliations

  1. School of Information Technologies, University of Sydney, Sydney, Australia

    Afaf Tareef, Yang Song, Weidong Cai & Dagan Feng

  2. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA

    Heng Huang

  3. Department of Electrical and Computer Engineering, Virginia Tech Research Center - Arlington, Arlington, USA

    Yue Wang

  4. Med-X Research Institute, Shanghai Jiaotong University, Shanghai, China

    Dagan Feng

  5. Department of Informatics, University of Albany State University of New York, Albany, USA

    Mei Chen

  6. Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Mei Chen

Authors
  1. Afaf Tareef
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  2. Yang Song
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  3. Weidong Cai
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  4. Heng Huang
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  5. Yue Wang
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  6. Dagan Feng
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  7. Mei Chen
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Corresponding author

Correspondence to Afaf Tareef .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Tareef, A. et al. (2015). Learning Shape-Driven Segmentation Based on Neural Network and Sparse Reconstruction Toward Automated Cell Analysis of Cervical Smears. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_43

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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