Automatic Counting for Live and Dead Cells from Trypan Blue-Stained Images by Image Analysis Based on Adaptive K-Means Clustering

Abstract

Computer-assisted image analysis can be employed to reduce the time consumed in the routine task such as cell counting. This study aimed to establish a method to perform this routine task based on an image analysis to automatically count live and dead cells after staining with trypan blue dye. Gray scale conversion and morphological operation were applied to the input images to enhance the image quality before image segmentation, then adaptive k-means clustering was applied to classify the groups of live and dead cells. Circular Hough transform and object labelling were carried out to identify the number of each cell type. The counting results from the proposed method were compared with the counting of three experts and the ImageJ software. The results showed that the proposed method had very high correlation with the results of the three experts in counting live cells (R²>0.95) and was better than the counting results achieved by ImageJ. The number of dead cells counted by our program was in good agreement with the experts’ counting (R²>0.64). In conclusion, this study suggests that using new image analysis program can be confidently substituted for a manual counting in routine cell counting