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

Chen Pan, Yi Fang, Xiang-guo Yan, and Chong-xun Zheng
International Journal of Control, Automation, and Systems, vol. 4, no. 5, pp.637-644, 2006

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.

Keyword : Blood and bone marrow, image segmentation, mean shift, SVM, watershed transform.

 
 
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