## 1 Introduction  
The immune system, which is the third line of defense of the human body, protects the body from viruses, bacteria, and pathogens. This natural defense identifies and eliminates abnormal cells, such as tumor cells. The immune system consists of immune organs, immune cells, and immune molecules. White blood cells (WBCs) are the principal components of immune cells and play an important role in our body's immunity. In fact, WBCs normally have a constant concentration in the human blood. If the amount of WBC exceeds the normal range, then health problems may occur. The morphological analysis of WBCs is one of the basic steps of blood pathological analysis. Morphological analysis is traditionally performed manually, thus making this a tedious and time consuming process, even for an expert. Furthermore, morphological analysis is limited to the professional knowledge and eyesight of a pathologist. Automatic methods have been developed in recent years to overcome the limitations of manual methods, which can cause calculation inaccuracies. Automatic devices use the principle of the light scattering method to calculate red blood cells (RBCs) and WBCs [1]. The counting procedure of the light scattering method is invisible and can only achieve routine classification, merely counting the number of RBCs and WBCs. Actually, WBCs can be divided into five types, namely, basophils, eosinophils, neutrophils, lymphocytes, and monocytes [2,3]. The first three types are granular types, and the last two types are non-granular. Distinguishing these types by using the light scattering method proved unsuccessful in this study. Thus, we used image analysis, which can provide human-like assessments, to overcome this problem. Image analysis identifies abnormal cells and adopts the diagnosis experience summarized by pathologists. This method uses the high resolution and flexible extracted features of computer vision to improve work efficiency and accuracy. Researchers are currently focused on developing a system that can automatically identify WBCs by using blood images. However, accurate WBC segmentation before classification still poses challenges. WBC segmentation is an important part of the WBC classification system, and the segmentation results directly affect the accuracy of cell recognition. Researchers have proposed several useful methods for achieving accurate segmentation results. [PERSON] _et al._[4, 5, 6, 7] divided WBC segmentation into two parts: one for nucleus segmentation and the other for cytoplasm segmentation. The nuclei of WBCs are the areas of high contrast and are relatively easy to segment in blood smear image. Thus, most of the traditional methods [4, 5, 6, 7] first segment the nucleus part of the image before the cytoplasm part. Considerable research has been done in this field in recent years. All these methods can be divided into five categories: threshold-based methods, learning-based methods, active-contour-based methods, metaheuristic-based methods, and saliency-based methods [8]. Threshold-based methods include the region growing method, watershed method, [PERSON]'s method, and their combination. [PERSON] _et al._[6] segmented the nucleus and then applied the region growth of color information according to certain rules to extract the cytoplasm. [PERSON] _et al._[9] proposed a method based on the thresholding of the cyan, magenta, yellow, key plate (CMYK) color space. Threshold-based methods have high running speed and reliable performance for unified images. However, the performance of threshold-based methods is inconsistent for non-unified images. Learning-based methods include supervised methods, including support vector machine (SVM), artificial neural network, and unsupervised methods such ask-means clustering and Fuzzy c-means. [PERSON] _et al_. combined the expectation-maximization clustering and SVM to segment the WBCs [10]. The performance of learning-based methods mainly depends on the selection of color feature vectors. Active-contour-based methods include level sets and snakes.