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%.
As an imaging technology used in remote sensing, hyperspectral imaging can provide more information than traditional optical imaging of blood cells. In this paper, an AOTF based microscopic hyperspectral imaging system is used to capture hyperspectral images of blood cells. In order to achieve the segmentation of red blood cells, Gaussian process using squared exponential kernel function is applied first after the data preprocessing to make the preliminary segmentation. The derivative spectrum with spectral angle mapping algorithm is then applied to the original image to segment the boundary of cells, and using the boundary to cut out cells obtained from the Gaussian process to separated adjacent cells. Then the morphological processing method including closing, erosion and dilation is applied so as to keep adjacent cells apart, and by applying median filtering to remove noise points and filling holes inside the cell, the final segmentation result can be obtained. The experimental results show that this method appears better segmentation effect on human red blood cells.