The traditional statistical thresholding methods which directly construct the optimal threshold criterions by the class-variance have certain versatility, but lack the specificity of practical application in some cases. In order to select the optimal threshold for blood cell image segmentation and extract white blood cells nuclei, a simple and fast method based on cloud model was proposed. The method firstly generated the cloud models corresponding to white blood cells nuclei and blood cell image background respectively, and defined a new thresholding criterion by utilizing the hyper-entropy of cloud models, then obtained the optimal grayscale threshold by the maximization of this criterion, finally achieved blood cell image thresholding and white blood cells nuclei extraction. The experimental results indicate that, compared with the traditional methods including maximizing inter-class variance method, maximizing entropy method, minimizing error method, minimizing intra-class variance sum method, and minimizing maximal intra-class variance method, the proposed method is suitable for blood cell image thresholding, and it is reasonable and effective.