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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)
Publication Information
International Journal of Control, Automation, and Systems / v.4, no.5, 2006 , pp. 637-644 More about this Journal
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
Blood and bone marrow; image segmentation; mean shift; SVM; watershed transform;
Citations & Related Records

Times Cited By Web Of Science : 2  (Related Records In Web of Science)
Times Cited By SCOPUS : 2
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