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International Workshop on Machine Learning in Medical Imaging

MLMI 2015: Machine Learning in Medical Imaging pp 288–295Cite as

Computer-Assisted Diagnosis of Lung Cancer Using Quantitative Topology Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

Abstract

In this paper, we proposed a computer-aided diagnosis and analysis for a challenging and important clinical case in lung cancer, i.e., differentiation of two subtypes of Non-small cell lung cancer (NSCLC). The proposed framework utilized both local and topological features from histopathology images. To extract local features, a robust cell detection and segmentation method is first adopted to segment each individual cell in images. Then a set of extensive local features is extracted using efficient geometry and texture descriptors based on cell detection results. To investigate the effectiveness of topological features, we calculated architectural properties from labeled nuclei centroids. Experimental results from four popular classifiers suggest that the cellular structure is very important and the topological descriptors are representative markers to distinguish between two subtypes of NSCLC.

J. Huang—This work was partially supported by U.S. NSF IIS-1423056, CMMI-1434401, CNS-1405985.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Jiawen Yao, Dheeraj Ganti & Junzhou Huang

  2. Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA

    Xin Luo, Guanghua Xiao & Yang Xie

  3. Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA

    Shirley Yan

Authors
  1. Jiawen Yao
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  2. Dheeraj Ganti
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  3. Xin Luo
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  4. Guanghua Xiao
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  5. Yang Xie
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  6. Shirley Yan
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  7. Junzhou Huang
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Corresponding author

Correspondence to Junzhou Huang .

Editor information

Editors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Luping Zhou

  2. Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA

    Li Wang

  3. Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China

    Qian Wang

  4. Computer Science, Nanjing University, Nanjing, China

    Yinghuan Shi

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© 2015 Springer International Publishing Switzerland

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Yao, J. et al. (2015). Computer-Assisted Diagnosis of Lung Cancer Using Quantitative Topology Features. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_35

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  • DOI: https://doi.org/10.1007/978-3-319-24888-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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