International Journal of Control, Automation, and Systems

  • 제4권5호
  • /
  • Pages.637-644
  • /
  • 2006
  • /
  • 1598-6446(pISSN)
  • /
  • 2005-4092(eISSN)
제어로봇시스템학회 (Institute of Control, Robotics and Systems)
권호 표지

Robust Segmentation for Low Quality Cell Images from Blood and Bone Marrow

  • Pan Chen (School of Mathematics & Computer Engineering, Ninxia University) ;
  • Fang Yi (Institute of Biomedical Engineering, Xi'an Jiaotong University) ;
  • Yan Xiang-Guo (Institute of Biomedical Engineering, Xi'an Jiaotong University) ;
  • Zheng Chong-Xun (Institute of Biomedical Engineering, Xi'an Jiaotong University)
  • 발행 : 2006.10.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

키워드

참고문헌

  1. H. Hengen, S. Spoor, and M. Pandit, 'Analysis of blood and bone marrow smears using digital image processing techniques,' SPIE Medical Imaging, vol. 4684, pp. 624-635, February 2002
  2. H.-D. Cheng, X.-H. Jiang, Y. Sun, and J. Wang, 'Color image segmentation: Advances and prospects,' Pattern Recognition, vol. 34, no. 12, pp. 2259-2281, December 2001 https://doi.org/10.1016/S0031-3203(00)00149-7
  3. C. Kotopoulos and I. Pitas, 'Segmentation of ultrasonic images using support vector machines,' Pattern Recognition Letters, vol. 24, no. 4-5, pp. 715-727, 2003 https://doi.org/10.1016/S0167-8655(02)00177-0
  4. F. Melgani and L. Bruzzone, 'Classification of hyperspectral remote sensing images with support vector machines,' IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1778-1790, 2004
  5. D. Comaniciu and P. Meer, 'Mean shift: A robust approach toward feature space analysis,' IEEE Trans. on PAMI, vol. 24, no. 5, pp. 603- 619, 2002 https://doi.org/10.1109/34.1000236
  6. J. F. David, D. Comaniciu, and P. Meer, 'Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy,' IEEE Trans. on Information Technology in Biomedicine, vol. 4, no. 4, pp. 12-22, 2000
  7. C. Campbell, 'Algorithmic approaches to training support vector machines: A survey,' Proc. of European Symposium on Artificial Neural Networks, pp. 27-36, 2000
  8. V. Vapnik, Statistical Learning Theory, John Willey & Sons, New York, 1998
  9. Y.-Q. Zhan and D.-G. Shen, 'Design efficient support vector machine for fast classification,' Pattern Recognition, vol. 38, no. 1, pp. 157-161, 2005 https://doi.org/10.1016/j.patcog.2004.06.001
  10. G. Ratsch, T. Onoda, and K. R. Muller, 'Soft margins for adaBoost,' Machine Learning, vol. 42, pp. 87-320, 2001
  11. N. Malpica, C.O. Solorezano, J.J. Vaquero, and A. Santos, 'Applying watershed algorithms to the segmentation of clustered nuclei,' Cytometry, vol. 28, pp.289-297, 1997 https://doi.org/10.1002/(SICI)1097-0320(19970801)28:4<289::AID-CYTO3>3.0.CO;2-7
  12. M. J. Carlotto. 'Histogram analysis using a scale-space approach,' IEEE Trans. on PAMI, vol.9, no.1, pp.121-129, 1987