基于颜色模型极小值运算的细胞图像分割算法

为了能够快速准确地检测人类细胞形态及增殖分化等情况，提出一种高效自适应细胞图像的分割方法。首先提取图像六角锥体模型中特征明显的饱和度分量图像，并使用形态学重构、H-minima技术和图像增强技术对饱和度分量图像进行梯度修正；修正图像的梯度后，使用分水岭算法对图像进行分割；根据原图像灰度的一致性对分割结果进行区域合并，得到背景、细胞和细胞核3部分，最后使用形态学方法处理合并结果中的虚假噪点并平整区域边缘。实验结果表明，细胞与细胞核分割的准确度分别为0.978 8和0.967 7；Dice系数分别为0.938 8和0.937，实现了对医学显微细胞图像的精准分割。

Cell Image Segmentation Algorithm Based on Minimum Operation of Color Model

To detect the morphology, proliferation, and differentiation of human cells quickly and accurately, an efficient adaptive cell image segmentation method is proposed. First, the image of saturation with obvious characteristics is extracted from the Hue,Saturation Value (HSV) channel of the image, and then, morphological reconstruction, H-minima technology, and image enhancement technology are used to perform gradient correction on the image of saturation. After the gradient of the image is corrected, the watershed algorithm is used to perform segmentation. Next, the segmentation result is merged to obtain the background, cell, and nucleus according to the gray level consistency of the original image. Finally, post-morphological processing is used to remove the false noise in the merged result and flatten the edge of the region. The results of the segmentation test on cell images show that the accuracies of cell and cell nucleus segmentation are 0.978 8 and 0.967 7, respectively. The Dice coefficients are 0.938 8 and 0.937, which realizes the accurate segmentation of medical micro cell images.