A combined spatial-spectral method for automated white blood cells segmentation

To overcome the shortcomings in the traditional white blood cells (WBCs) identification methods based on the color or gray images captured by light microscopy, a microscopy hyperspectral imaging system was used to analyze the blood smears. The system was developed by coupling an acousto-optic tunable filter (AOTF) adapter to a microscopy and driven by a SPF Model AOTF controller, which can capture hyperspectral images from 550 nm to 1000 nm with the spectral resolution 2-5 nm. Moreover, a combined spatial-spectral algorithm is proposed to segment the nuclei and cytoplasm of WBCs from the microscopy hyperspectral images. The proposed algorithm is based on the pixel-wise improved spectral angle mapper (ISAM) segmentation, followed by the majority voting within the active contour model regions. Experimental results show that the accuracy of the proposed algorithm is 91.06% (nuclei) and 85.59% (cytoplasm), respectively, which is higher than that of the spectral information divergence (SID) algorithm because the new method can jointly use both the spectral and spatial information of blood cells.