Soft Computing

, Volume 10, Issue 1, pp 12–19

A novel white blood cell segmentation scheme based on feature space clustering

Authors

    • Department of Electronic EngineeringTsinghua University
  • Qing-Min Liao
    • Department of Electronic EngineeringTsinghua University
  • Yuan Xiong
    • Department of Electronic EngineeringTsinghua University
Focus

DOI: 10.1007/s00500-005-0458-z

Cite this article as:
Jiang, K., Liao, Q. & Xiong, Y. Soft Comput (2006) 10: 12. doi:10.1007/s00500-005-0458-z

Abstract

This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods.

Keywords

WBC segmentationFeature space clusteringScale-space filteringWatershed

Copyright information

© Springer-Verlag 2005