Counting of different classes of white blood cells in bone marrow smears can give pathologists valuable information regarding various cancers. But it is tedious to manually locate, identify, and count these classes of cells, even by skilled hands. This paper presents a novel approach for automatic detection of White Blood Cells in bone marrow microscopic images. Different from traditional color imaging method, we use multispectral imaging techniques for image acquisition. The combination of conventional digital imaging with spectroscopy can provide us with additional useful spectral information in common pathological samples. With our spectral calibration method, device-independent images can be acquired, which is almost impossible in conventional color imaging method. A novel segmentation algorithm using spectral operation is presented in this paper. Experiments show that the segmentation is robust, precise, with low computational cost and insensitive to smear staining and illumination condition. Once the nuclei and cytoplasm have been segmented, more than a hundred of features are extracted under the direction of a pathologist, including shape features, textural features and spectral ratio features. In pattern recognition, a maximum likelihood classifier (MLC) is implemented in a hierarchical tree. The classification results are also discussed. This paper is focused on image acquisition and segmentation.