A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection

White blood cell detection is one of the most basic and key steps in the automatic recognition system of white blood cells in microscopic blood images. Its accuracy and stability greatly affect the operating speed and recognition accuracy of the whole system. But there are only a few methods available for cell detection or segmentation due to the complexity of the microscopic images. This paper focuses on this issue. Based on the detailed analysis of the existing two methods--threshold segmentation followed by mathematical morphology (TSMM), and the fuzzy logic method--a new detection algorithm (NDA) based on fuzzy cellular neural networks is proposed. NDA combines the advantages of TSMM and the fuzzy logic method, and overcomes their drawbacks. With NDA, we can detect almost all white blood cells, and the contour of each detected cell is nearly complete. Its adaptability is strong and the running speed is expected to be comparatively high due to the easy hardware implementation of FCN. Experimental results show good performance.