利用云模型的血细胞图像阈值化方法

Blood cell image thresholding method using cloud model

经典统计阈值方法直接利用类方差构造最优阈值准则,具有一定的通用性,但在某些情况下缺乏实际应用的针对性。为了解决血细胞图像阈值化及白细胞核提取问题,提出了一种利用云模型的简单快速方法。该方法分别生成白细胞核和血细胞背景对应的云模型,利用各类云模型的超熵定义了新的阈值化准则,然后通过最大化该准则自动获取最优灰度阈值,最终完成血细胞图像二值化及白细胞核提取。实验结果表明,与Otsu法、最大熵法、最小误差法、最小类内方差和法以及最小极大类内方差法等方法相比,新方法更适合于血细胞图像分割,二值化效果好,白细胞核提取质量高,具有合理性和有效性。

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-elass variance method, the proposed method is suitable for blood cell image thresholding, and it is reasonable and effective.