Nowadays, the demand for segmenting different types of cells imaged by microscopes is increased tremendously. The requirements for the segmentation accuracy are becoming stricter. Because of the great diversity of cells, no traditional methods could segment various types of cells with adequate accuracy. In this paper, we aim to propose a generic approach that is capable of segmenting various types of cells robustly and counting the total number of cells accurately. To this end, we utilize the gradients of cells instead of intensity for cell segmentation because the gradients are less affected by the global intensity variations. To improve the segmentation accuracy, we utilize the Gabor filter to increase the intensity uniformity of the gradient image. To get the optimal segmentation, we utilize the slope difference distribution based threshold selection method to segment the Gabor filtered gradient image. At last, we propose an area-constrained ultimate erosion method to separate the connected cells robustly. Twelve types of cells are used to test the proposed approach in this paper. Experimental results showed that the proposed approach is very promising in meeting the strict accuracy requirements for many applications.

如今，对通过显微镜成像的不同类型细胞进行分割的需求大大增加。对分割精度的要求越来越严格。由于细胞的多样性，没有传统方法可以足够准确地分割各种类型的细胞。在本文中，我们旨在提出一种通用方法，该方法能够稳健地分割各种类型的细胞并准确计算细胞总数。为此，我们利用细胞梯度而不是强度进行细胞分割，因为梯度受全局强度变化的影响较小。为了提高分割精度，我们利用 Gabor 滤波器来增加梯度图像的强度均匀性。为了获得最佳分割，我们利用基于斜率差异分布的阈值选择方法对 Gabor 滤波梯度图像进行分割。最后，我们提出了一种区域约束的最终侵蚀方法来稳健地分离连接的单元格。本文使用 12 种类型的细胞来测试所提出的方法。实验结果表明，所提出的方法在满足许多应用的严格精度要求方面非常有前途。