Leukocytes count in the blood smear images plays an important role in identifying the overall health of the patient. The major steps involved in leukocytes counting system are segmentation and counting. However, the counting accuracy is greatly affected due to the morphological diversity of cells, the presence of staining artifacts and the overlapped cells. Therefore, this paper introduces a new framework to segment and counting of leukocytes. To segment leukocytes, an edge strength-based Grabcut method has been proposed. Later, the leukocyte region including the overlapped cells was counted using the novel gradient circular hough transform (GCHT) method. The research work was performed on ALL-IDB and Cellavision datasets. The proposed segmentation method has yielded high precision, recall and $f$-measure compared to the state-of-the-art methods. Additionally, comparison analysis was performed between the region count obtained using the existing and the GCHT method. The overall experimental results of the work showed that the proposed framework produced more accuracy in counting the leukocytes.

血液涂片图像中的白细胞计数在识别患者的整体健康方面起着重要作用。白细胞计数系统涉及的主要步骤是分割和计数。但是，由于细胞的形态多样性，染色伪影和重叠细胞的存在，极大地影响了计数精度。因此，本文介绍了一种新的白细胞分割和计数框架。为了分割白细胞，已经提出了基于边缘强度的Grabcut方法。随后，使用新型梯度圆霍夫变换（GCHT）方法对包括重叠细胞的白细胞区域进行计数。研究工作在ALL-IDB和Cellavision数据集上进行。提出的分割方法具有很高的精度，召回率和$F$测量与最新方法相比。另外，在使用现有方法和GCHT方法获得的区域计数之间进行了比较分析。这项工作的整体实验结果表明，提出的框架在计数白细胞方面产生了更高的准确性。