Background and Objective: The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images of blood is designed.

Methods: The proposed algorithm uses the ellipse detection approach to approximate the presence of WBCs in the Blood. A newly designed artificial electric field algorithm with novel velocity and position bound (AEFA-C) is employed for this purpose. The problem of detection of WBCs is transformed into an optimization problem where the random candidate solutions (ellipses) are efficiently mapped. These candidate ellipses are mapped onto the edge map of the smear image, and a complete mapping is obtained using the AEFA-C algorithm.

Results: The effectiveness of the AEFA-C based detector is tested over the 60 smear images of the blood, having all the five types of WBCs or leukocytes. The developed algorithm obtained an overall detection accuracy of 96.90%. Further, the robustness test is performed on the same dataset which justifies that the technique can handle the different noises with the detection accuracy of 90.33%. Also, the comparative study of the proposed detection algorithm with the state-of-art detection algorithms is carried out.

Conclusions: The experimental results demonstrate the efficiency of the proposed scheme for the detection of the WBCs in terms of detection accuracy, stability, and robustness and its outperformance over the state-of-art algorithms.

背景与目的：白细胞（WBC）的自动检测和计数在血液系统疾病的诊断中起着至关重要的作用。计算机辅助方法在WBC的检测中很普遍，因为手动过程涉及多种复杂性。在本文中，设计了一种完整的自动检测算法，以识别嵌入在混乱且复杂的血液涂片图像中的白细胞。

方法：所提出的算法使用椭圆检测方法来近似估计血液中白细胞的存在。为此，采用了一种新设计的具有新颖速度和位置限制的人工电场算法（AEFA-C）。WBC的检测问题被转换为优化问题，在该问题中有效地映射了随机候选解（椭圆）。将这些候选椭圆映射到涂片图像的边缘图上，并使用AEFA-C算法获得完整的映射。

结果：基于AEFA-C的检测器在60种血液涂片图像上进行了测试，这些图像具有所有五种类型的WBC或白细胞。所开发的算法获得了96.90％的整体检测精度。此外，在同一数据集上执行了鲁棒性测试，证明该技术可以以90.33％的检测精度处理不同的噪声。另外，还对提出的检测算法与最新的检测算法进行了比较研究。

结论：实验结果证明了所提方案在检测准确性，稳定性和鲁棒性方面的有效性，并且优于现有算法。