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BioMed Research International
Volume 2017, Article ID 9718386, 14 pages
https://doi.org/10.1155/2017/9718386
Research Article

Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition

Software College, Northeastern University, Shenyang 110819, China

Correspondence should be addressed to Huiyan Jiang; nc.ude.uen.liam@gnaijyh

Received 5 January 2017; Accepted 13 June 2017; Published 17 July 2017

Academic Editor: Satoshi Maruyama

Copyright © 2017 Wenbo Pang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. Xia, H. Jiang, H. Liu, and D. Yi, “A novel hepatocellular carcinoma image classification method based on voting ranking random forests,” Computational and Mathematical Methods in Medicine, vol. 2016, Article ID 2628463, 8 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. R. Stoklasa, T. Majtner, and D. Svoboda, “Efficient k-NN based HEp-2 cells classifier,” Pattern Recognition, vol. 47, no. 7, pp. 2409–2418, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. S. H. Soomro, L. Xiao, and B. N. Soomro, “Hyperspectral image classification via Elastic Net Regression and bilateral filtering,” in Proceedings of the 3rd IEEE International Conference on Progress in Informatics and Computing, PIC 2015, pp. 56–60, chn, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Krawczyk, M. Galar, Ł. Jeleń, and F. Herrera, “Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy,” Applied Soft Computing Journal, vol. 38, pp. 714–726, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Nakano, B. T. Nukala, S. Zupancic et al., “Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier,” in Proceedings of the 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016, jpn, June 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Thibault, J. Angulo, and F. Meyer, “Advanced statistical matrices for texture characterization: application to cell classification,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 3, pp. 630–637, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Zhang, F. Li, M. Zhao, L. Zhang, and S. Yan, “Robust neighborhood preserving projection by nuclear/L2, 1-norm regularization for image feature extraction,” IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1607–1622, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  8. S. B. Ginsburg, G. Lee, S. Ali, and A. Madabhushi, “Feature importance in nonlinear embeddings (FINE): applications in digital pathology,” IEEE Transactions on Medical Imaging, vol. 35, no. 1, pp. 76–88, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Chen, B. Zou, and M. Huang, “Influence of intensity feature on ROI extraction,” Journal of Central South University (Science and Technology), vol. 1, no. 33, 2012. View at Google Scholar
  10. A. H. Beck, A. R. Sangoi, S. Leung et al., “Imaging: systematic analysis of breast cancer morphology uncovers stromal features associated with survival,” Science Translational Medicine, vol. 3, no. 108, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Li, J. Yin, and Z. Lu, “Multiclass boosting SVM using different texture features in HEp-2 cell staining pattern classification,” in Proceedings of the 21st International Conference on. IEEE Pattern Recognition (ICPR), pp. 170–173, 2012.
  12. S. Di Cataldo, A. Bottino, E. Ficarra et al., “Applying textural features to the classification of HEp-2 cell patterns in IIF images,” in Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012, pp. 3349–3352, 2012. View at Scopus
  13. O. S. Al-Kadi, “Texture measures combination for improved meningioma classification of histopathological images,” Pattern Recognition, vol. 43, no. 6, pp. 2043–2053, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, no. 1, pp. 93–104, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. G. B. Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, vol. 74, no. 1–3, pp. 155–163, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Xie, K. Xu, L. Liu, and Y. Xiong, “3D shape segmentation and labeling via extreme learning machine,” Computer Graphics Forum, vol. 33, no. 5, pp. 85–95, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 42, no. 2, pp. 513–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Zarella D, D. Breen E, A. Plagov et al., “An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides,” Journal of Pathology Informatics, vol. 6, no. 1, 33 pages, 2015. View at Google Scholar
  19. V. Sebastian, A. Unnikrishnan, and K. Balakrishnan, Gray level co-occurrence matrices: Generalisation and some new features, arXiv preprint, 2012, arXiv:1205.4831.
  20. Y. Zhao, W. Jia, R.-X. Hu, and H. Min, “Completed robust local binary pattern for texture classification,” Neurocomputing, vol. 106, pp. 68–76, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Lindeberg, “Scale invariant feature transform,” Scholarpedia, vol. 7, no. 5, article 10491, 2012. View at Publisher · View at Google Scholar
  22. J. Jing, H. Zhang, J. Wang, P. Li, and J. Jia, “Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method,” Journal of the Textile Institute, vol. 104, no. 1, pp. 18–27, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Novaković, “Toward optimal feature selection using ranking methods and classification algorithms,” Yugoslav Journal of Operations Research, vol. 21, no. 1, 2016. View at Google Scholar · View at MathSciNet
  24. G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Computers & Electrical Engineering, vol. 40, no. 1, pp. 16–28, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Begum, S. P. Bera, D. Chakraborty, and et al, “Breast cancer detection using feature selection and active learning,” in Proceedings of the International Conference on Advancement of Computer Communication and Electrical Technology (ACCET 2016). Computer, Communication and Electrical Technolog, vol. 43, CRC Press, West Bengal, India, 2017. View at Publisher · View at Google Scholar
  26. S. Gu, R. Cheng, and Y. Jin, “Feature selection for high-dimensional classification using a competitive swarm optimizer,” Soft Computing, pp. 1–12, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Ngiam, Z. Chen, A. S. Bhaskar et al., “Sparse filtering,” Advances in Neural Information Processing Systems, pp. 1125–1133, 2011. View at Google Scholar
  28. H. Irshad, A. Veillard, L. Roux, and D. Racoceanu, “Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential,” IEEE Reviews in Biomedical Engineering, vol. 7, pp. 97–114, 2014. View at Publisher · View at Google Scholar · View at Scopus