International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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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)
  • Published : 2006.10.01
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Abstract

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.

Keywords

References

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