Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 2018, Article ID 5098973, 11 pages
https://doi.org/10.1155/2018/5098973
Research Article

Leukocyte Image Segmentation Using Novel Saliency Detection Based on Positive Feedback of Visual Perception

1China Jiliang University, Hangzhou, Zhejiang 310018, China
2Department of Hematology at the First Hospital Affiliated to Medical College, Zhejiang University, Hangzhou, Zhejiang 310003, China
3School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China

Correspondence should be addressed to Chen Pan; nc.ude.uljc@619cp

Received 24 February 2017; Revised 8 November 2017; Accepted 21 November 2017; Published 1 February 2018

Academic Editor: Maria Lindén

Copyright © 2018 Chen Pan 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. Z. Yu, H. S. Wong, and G. Wen, “A modified support vector machine and its application to image segmentation,” Image and Vision Computing, vol. 29, no. 1, pp. 29–40, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Pan, D. S. Park, Y. Yang, and H. M. Yoo, “Leukocyte image segmentation by visual attention and extreme learning machine,” Neural Computing and Applications, vol. 21, no. 6, pp. 1217–1227, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440, Boston, MA, USA, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Zheng, Y. Wang, G. Wang, and Z. Chen, “A novel algorithm based on visual saliency attention for localization and segmentation in rapidly-stained leukocyte images,” Micron, vol. 56, pp. 17–28, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Matrin, “Microsaccades: small steps on a long way,” Vision Research, vol. 49, no. 20, pp. 2415–2441, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. G.-B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Learning and Cybernetics, vol. 2, no. 2, pp. 107–122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. J. W. Zhao, Z. H. Zhou, and F. L. Cao, “Human face recognition based on ensemble of polyharmonic extreme learning machine,” Neural Computing and Applications, vol. 24, no. 6, pp. 1317–1326, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Borji, M. M. Cheng, H. Z. Jiang, and J. Li, “Salient object detection: a benchmark,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5706–5722, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. X. D. Hou and L. Q. Zhang, “Saliency detection:a spectral residual approach,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, Minneapolis, MN, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Goferman, L. Z. Manor, and A. Tal, “Context-aware saliency detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1915–1926, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. M. M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S. M. Hu, “Global contrast based salient region detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569–582, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Shi, Q. Yan, L. Xu, and J. Jia, “Hierarchical image saliency detection on extended CSSD,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 4, pp. 717–729, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Siva, C. Russell, T. Xiang, and L. Agapito, “Looking beyond the image: unsupervised learning for object saliency and detection,” in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3238–3245, Portland, OR, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Na, L. U. Huchuan, R. Xiang, and M.-H. Yang, “Salient object detection via bootstrap learning,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1884–1892, Boston, MA, USA, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Huang, J. Qi, H. Lu, L. Zhang, and X. Ruan, “Salient object detection via multiple instance learning,” IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1911–1922, 2017. View at Publisher · View at Google Scholar
  16. L. Zhang, J. Li, and H. Lu, “Saliency detection via extreme learning machine,” Neurocomputing, vol. 218, no. 8, pp. 103–112, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Zhu, S. Liang, Y. Wei, and J. Sun, “Saliency optimization from robust background detection,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2814–2821, Columbus, OH, USA, June 2014.
  18. R. D. Labati, V. Piuri, and F. Scotti, “ALL-IDB: the acute lymphoblastic leukemia image database for image processing,” in Proc. of the 2011 IEEE Int. Conf. on Image Processing (ICIP 2011), pp. 2045–2048, Brussels, Belgium, September 2011.