A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering

Abstract

In this paper, a novel white blood cell (WBC) segmentation scheme using scale-Space filtering and watershed clustering is proposed. 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 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. By using feature space clustering technique, this scheme successfully avoids the variety and complexity in image space, and can effectively extract various WBC regions from cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs really well and HSV space is more appropriate than RGB space in WBC segmentation due to its low correlation.

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