Computer-Assisted Diagnosis of Lung Cancer Using Quantitative Topology Features

  • Jiawen Yao
  • Dheeraj Ganti
  • Xin Luo
  • Guanghua Xiao
  • Yang Xie
  • Shirley Yan
  • Junzhou HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)


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.


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  1. 1.
    Anagnostou, V.K., Dimou, A.T., Botsis, T., Killiam, E.J., Gustavson, M.D., Homer, R.J., Boffa, D., Zolota, V., Dougenis, D., Tanoue, L., et al.: Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay. Cancer 118(6), 1607–1618 (2012)CrossRefGoogle Scholar
  2. 2.
    Wang, H., Xing, F., Su, H., Stromberg, A., Yang, L.: Novel image markers for non-small cell lung cancer classification and survival prediction. BMC Bioinformatics 15(1), 310 (2014)CrossRefGoogle Scholar
  3. 3.
    Zhang, X., Su, H., Yang, L., Zhang, S.: Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5361–5368 (2015)Google Scholar
  4. 4.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 6, 610–621 (1973)CrossRefGoogle Scholar
  5. 5.
    Basavanhally, A.N., Ganesan, S., Agner, S., Monaco, J.P., Feldman, M.D., Tomaszewski, J.E., Bhanot, G., Madabhushi, A.: Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Transactions on Biomedical Engineering 57(3), 642–653 (2010)CrossRefGoogle Scholar
  6. 6.
    Gabor, D.: Theory of communication. part 1: The analysis of information. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering 93(26), 429–441 (1946)Google Scholar
  7. 7.
    Wienert, S., Heim, D., Saeger, K., Stenzinger, A., Beil, M., Hufnagl, P., Dietel, M., Denkert, C., Klauschen, F.: Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Scientific reports 2 (2012)Google Scholar
  8. 8.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)CrossRefGoogle Scholar
  9. 9.
    Grady, L., Schwartz, E.L.: Isoperimetric graph partitioning for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(3), 469–475 (2006)CrossRefGoogle Scholar
  10. 10.
    Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., et al.: Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology 7(10), R100 (2006)CrossRefGoogle Scholar
  11. 11.
    Tabesh, A., Teverovskiy, M., Pang, H.Y., Kumar, V.P., Verbel, D., Kotsianti, A., Saidi, O.: Multifeature prostate cancer diagnosis and gleason grading of histological images. IEEE Transactions on Medical Imaging 26(10), 1366–1378 (2007)CrossRefGoogle Scholar
  12. 12.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefGoogle Scholar
  13. 13.
    Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In: IEEE International Symposium on Biomedical Imaging, pp. 496–499. IEEE (2008)Google Scholar
  14. 14.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. Journal of statistical software 33(1), 1 (2010)CrossRefGoogle Scholar
  15. 15.
    Huang, J., Zhang, S., Metaxas, D.: Efficient MR image reconstruction for compressed MR imaging. Medical Image Analysis 15(5), 670–679 (2011)CrossRefGoogle Scholar
  16. 16.
    Huang, J., Zhang, T., Metaxas, D.: Learning with structured sparsity. The Journal of Machine Learning Research 12, 3371–3412 (2011)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Liu, X., Zhao, G., Yao, J., Qi, C.: Background subtraction based on low-rank and structured sparse decomposition. IEEE Transactions on Image Processing 24(8), 2502–2514 (2015)MathSciNetCrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Jiawen Yao
    • 1
  • Dheeraj Ganti
    • 1
  • Xin Luo
    • 2
  • Guanghua Xiao
    • 2
  • Yang Xie
    • 2
  • Shirley Yan
    • 3
  • Junzhou Huang
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of Clinical ScienceThe University of Texas Southwestern Medical CenterDallasUSA
  3. 3.Department of PathologyThe University of Texas Southwestern Medical CenterDallasUSA

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