基于细胞核引导的明场显微图像细胞分割方法

Nucleus-Guided Cell Segmentation Method for Brightfield Micrographs

针对明场显微细胞图像存在边缘弱、背景非均匀和细胞形状不规则等特点导致细胞分割困难的问题,提出一种基于荧光细胞核引导的明场显微图像细胞分割方法。首先,利用荧光细胞核质心确定明场单细胞局部显微图像,对局部明场显微细胞图像进行双重高斯滤波,以减弱非均匀背景的影响,采用顶帽变换增加图像的对比度,并采用二维最大类间方差分割方法以增强算法的抗噪性;其次,对完整的明场显微细胞图像进行双重滤波和顶帽变换预处理后,采用二维最大类间方差法进行全局分割,以增强局部分割丢失的细胞轮廓信息,解决细胞形状不规则导致的分割不准确问题;最后,将局部和全局分割的结果融合后采用分水岭变换进行二次分割,以提高对粘连性细胞的分割精度。在Hela细胞图像集上进行验证实验,得到明场细胞分割的精确率、召回率和F值分别为0.960、0.984和0.971,优于现有相关算法,验证了所提方法的高准确性和鲁棒性。

To address the issue of cell segmentation challenges caused by weak edges,uneven backgrounds,and irregular cell shape in brightfield microscopic images,we suggest a cell segmentation method for brightfield microscopic images based on fluorescent nucleus guidance.First,the fluorescent nuclear centroid determines the local microscopic image of a single cell in the brightfield,the double Gaussian filtering reduces the impact of nonuniform background,the top-hat transform enhances the contrast of the images,and the two-dimensional maximum interclass variance segmentation method enhances the antinoise performance of the algorithm.Second,the complete brightfield microscopic cell image is preprocessed using double filtering and top-hat transformation.This is followed by global segmentation using the twodimensional maximum interclass variance method to enhance the lost cell contour information in local segmentation,which is beneficial to solve the inaccurate segmentation problem caused by irregular cell shape.To increase the segmentation accuracy of sticky cells when local and global findings are combined,the watershed transformation is then employed for secondary segmentation.Through the verification experiment on the Hela cell image set,the accuracy,recall rate,and F value of the brightfield cell segmentation are 0.960,0.984,and 0.971,respectively,which are better than the existing algorithms;the results confirm the high accuracy and robustness of the proposed method.