## 4 Experimental Results  
Several methods for WBC segmentation have been proposed. However, the majority of these methods work only in specific databases wherein images are captured under certain circumstances to control light and other influencing factors. Our database was constructed by using an Olympus BX51 microscope in the cell lab at the Second Hospital of Shandong University under different circumstances. Our database consisted of 300 stained blood smear images. The images of the database were captured in three sections, and relatively long time gaps exist between each section. Approximately 100 images were obtained in each section of WBC image acquisition. In each acquisition section, the WBC images were captured under the same light condition from different  
Figure 11: **(a)** Result of the whole segmentation of the WBC and RBC regions; **(b)** Preliminary result of the entire WBC region segmentation. Figure 12: **(a)** WBC segmentation result after denoising; **(b)** Result of WBC nucleus and cytoplasm segmentation. blood smears. Between each acquisition section the light condition differs from each other to some extent. The blood smears used for acquisition were stained with the same dyeing technique, and the dyeing time and the smear thick of each smear was slightly different according to the experience of the hematologist. Every WBC image has at least one WBC, and a number of them may have several WBCs. As previously mentioned, five types of WBCs can be found in the database. Every WBC image contains at least one type of WBC, and a number of them may contain several types of WBCs. The database has a total of 690 WBCs, which consists of 87 basophils, 94 eosinophils, 124 neutrophils, 203 lymphocytes, and 182 monocytes. Some sample images in our database are shown in Figure 13. The samples show that the color of each image differs from one another. Different databases differ in background color and dyeing conditions. Thus, not all of the previously proposed segmentation methods mentioned in the Introduction perform well all the time. The different conditions of the blood smear image hinder WBC segmentation. By conducting various experiments, we developed a method to segment WBC. Our method consists of two parts: (1) the segmentation of the nucleus and RBC; and (2) the segmentation of the WBC cytoplasm. By acquiring the whole WBC region and the nucleus region, the cytoplasm region could be obtained by a simple subtraction operation. During processing, we assumed that every input image had at least one WBC; no inferences were sizable compared with the WBC contained in the image. We also ignored the overlapping WBCs issues. Figure 13: Sample images in the database. We present the performance test of the proposed WBC segmentation method on our database in this section, as discussed in Section 3. A total of 300 blood smear images were used to test the performance. We applied our method on each of the 300 images and then compared the results with the results of manual segmentation. The percentages of accuracy were calculated accordingly. Our database obtained an average overall accuracy of 95.7% for nucleus segmentation and 91.3% for cytoplasm segmentation. In this evaluation, the segmentation result is considered accurate when the auto-detected boundary closely matches the manually traced boundary. We compared our method with the region growing method [6] and the snake method [8] to further evaluate the performance of our method. Table 1 shows the experimental results, wherein A1 denotes the ratio of the number of correctly detected WBCs to the total number of detected WBCs (Equation (7)). When A1 = 1, the WBCs detected in the microscopic images are all real WBCs, and no false detection occurs. The evaluation parameter A2 denotes the ratio of the number of WBCs detected to the total number of WBCs, as shown by Equation (8). When A2 = 1, no leak detection occurs. Equation (9) demonstrates the computation of false and leak detection ratios. A1 and A2 denote the detection accuracy of the entire system.