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The identification of white blood cells was important as it provided diagnosis information of different kinds of diseases. However, traditional light microscopy based leukocyte cells recognition and segmentation methods usually inaccurate. This paper proposed a hybrid algorithm applied mathematical support vector machine cells screening algorithm combined with BandMax and spectral angle mapping for white blood cell segmentation, that was, it treated BandMax and spectral angle mapping as a new preprocessing method to divide the boundaries between cells, and then used support vector machine cells screening algorithm to segment the hyperspectral cell images more efficiently and precisely than traditional segmentation algorithms. Experimental results shown that the hybrid algorithm provided higher classification accuracy than traditional methods on improving the classification accuracy and effective extraction of white blood cells. By combing both spatial and spectral features, this strategy had been successfully tested for classifying objects among leukocytes, erythrocytes and serums in raw samples, including spectral features reached higher accuracy than any single algorithm cases, with a maximum improvement of nearly 26.82%.
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