Discriminative Feature Selection by Optimal Manifold Search for Neoplastic Image Recognition

  • Hayato ItohEmail author
  • Yuichi Mori
  • Masashi Misawa
  • Masahiro Oda
  • Shin-Ei Kudo
  • Kensaku Mori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)


An endocytoscope provides ultramagnified observation that enables physicians to achieve minimally invasive and real-time diagnosis in colonoscopy. However, great pathological knowledge and clinical experiences are required for this diagnosis. The computer-aided diagnosis (CAD) system is required that decreases the chances of overlooking neoplastic polyps in endocytoscopy. Towards the construction of a CAD system, we have developed texture-feature-based classification between neoplastic and non-neoplastic images of polyps. We propose a feature-selection method that selects discriminative features from texture features for such two-category classification by searching for an optimal manifold. With an optimal manifold, where selected features are distributed, the distance between two linear subspaces is maximised. We experimentally evaluated the proposed method by comparing the classification accuracy before and after the feature selection for texture features and deep-learning features. Furthermore, we clarified the characteristics of an optimal manifold by exploring the relation between the classification accuracy and the output probability of a support vector machine (SVM). The classification with our feature-selection method achieved 84.7% accuracy, which is 7.2% higher than the direct application of Haralick features and SVM.


Feature selection Manifold learning Texture feature Convolutional neural network Endocytoscopic images Automated pathological diagnosis 



Parts of this research were supported by the Research on Development of New Medical Devices from the Japan Agency for Medical Research and Development (No. 18hk0102034h0103), and MEXT KAKENHI (No. 26108006, No. 17H00867).


  1. 1.
    Absil, P.A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2009)zbMATHGoogle Scholar
  2. 2.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  4. 4.
    Edelman, A., Arias, T., Smith, S.: The geometry of algorithms with orthogonality constraints. SIAM J. Matrix Anal. Appl. 20(2), 303–353 (1998)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Fukui, K., Maki, A.: Difference subspace and its generalization for subspace-based methods. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2164–2177 (2015)CrossRefGoogle Scholar
  6. 6.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)zbMATHGoogle Scholar
  7. 7.
    Fukunaga, K., Koontz, W.L.G.: Application of the Karhunen-Loéve expansion to feature selection and ordering. IEEE Trans. Comput. C–19(4), 311–318 (1970)CrossRefGoogle Scholar
  8. 8.
    Hamm, J., Lee, D.: Grassmann discriminant analysis: a unifying view on subspace-based learning. In: Proceedings of International Conference on Machine Learning, pp. 376–383 (2008)Google Scholar
  9. 9.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst., Man, Cybern. 3(6), 610–621 (1973). Scholar
  10. 10.
    Harandi, M., Sanderson, C., Shen, C., Lovell, B.: Dictionary learning and sparse coding on Grassmann manifolds: an extrinsic solution. In: Proceedings of The IEEE International Conference on Computer Vision, pp. 3120–3127 (2013)Google Scholar
  11. 11.
    Iijima, T.: Theory of pattern recognition. In: Electronics and Communications in Japan, pp. 123–134 (1963)Google Scholar
  12. 12.
    Itoh, H., Mori, Y., Misawa, M., Oda, M., Kudo, S.E., Mori, K.: Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis. In: Proceedings of SPIE Medical Imaging (2018, in Press)Google Scholar
  13. 13.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  14. 14.
    Jollife, I.T.: Principal Component Analysis. Springer, New York (2002). Scholar
  15. 15.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105 (2012)Google Scholar
  16. 16.
    Lecun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  17. 17.
    Maeda, K.: From the subspace methods to the mutual subspace method. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds.) Computer Vision. SCI, vol. 285, pp. 135–156. Springer, Berlin (2010). Scholar
  18. 18.
    Mori, Y., et al.: Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy 48, 1110–1118 (2016)CrossRefGoogle Scholar
  19. 19.
    Mori, Y., et al.: Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy. Gastrointest. Endosc. 81, 621–629 (2015)CrossRefGoogle Scholar
  20. 20.
    Oja, E.: Subspace Methods of Pattern Recognition. Research Studies Press, Boston (1983)Google Scholar
  21. 21.
    Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10, 61–74 (1999). MIT PressGoogle Scholar
  22. 22.
    Shigenaka, R., Raytchev, B., Tamaki, T., Kaneda, K.: Face sequence recognition using Grassmann distances and Gassmann kernels. In: Proceedings of International Joint Conference on Neural Networks, pp. 1–7 (2012)Google Scholar
  23. 23.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations (2015)Google Scholar
  24. 24.
    Slama, R., Wannous, H., Daoudi, M., Srivastava, A.: Accurate 3D action recognition using learning on the Grassmann manifold. Pattern Recognit. 48(2), 556–567 (2015)CrossRefGoogle Scholar
  25. 25.
    Tamaki, T., et al.: Computer-aided colorectal tumor classification in NBI endoscopy using CNN features. In: Proceedings of Korea-Japan Joint Workshop on Frontiers of Computer Vision (2016)Google Scholar
  26. 26.
    Tamaki, T., et al.: Computer-aided colorectal tumor classification in NBI endoscopy using local features. Mediacal Image Anal. 17, 78–100 (2013)CrossRefGoogle Scholar
  27. 27.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  28. 28.
    Watanabe, S., Pakvasa, N.: Subspace method of pattern recognition. In: Proceedings of of the 1st International Joint Conference of Pattern Recognition (1973)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hayato Itoh
    • 1
    Email author
  • Yuichi Mori
    • 2
  • Masashi Misawa
    • 2
  • Masahiro Oda
    • 1
  • Shin-Ei Kudo
    • 2
  • Kensaku Mori
    • 1
    • 3
    • 4
  1. 1.Graduate School of InformaticsNagoya UniversityNagoyaJapan
  2. 2.Digestive Disease CenterShowa University Northern Yokohama HospitalYokohamaJapan
  3. 3.Information Technology CenterNagoya UniversityNagoyaJapan
  4. 4.Research Center for Medical BigdataNational Institute of InformaticsTokyoJapan

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