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
Highlights
Computer-assisted image analysis has been introduced in recent years in medical and biological research to make image-related work easier in both qualitative and quantitative ways (Merino et al.,2018; Kharghanian and Ahmadyfard, 2012; Poostchi et al, 2018)
Some studies found that the adaptive k-means clustering required an accurate pre-processing process in order to obtain an accurate cell classification, but when this process was followed, the results were much better than standard k-means clustering (Moftah et al, 2014)
In our study, using the circular Hough transform after segmentation from the adaptive k-means clustering, the live cells were detected and extracted from the dead cells and the background as shown in Fig. 6, in which it can be noted that the shape of the live cells is circular and they are brighter than the dead cells, indicating that the circular Hough transform can detect live cells efficiently
Summary
Computer-assisted image analysis has been introduced in recent years in medical and biological research to make image-related work easier in both qualitative and quantitative ways (Merino et al.,2018; Kharghanian and Ahmadyfard, 2012; Poostchi et al, 2018). A reliable image analysis method for counting cells would be advantageous and useful. The circular Hough transform is a frequently used method for detecting circular objects in an image (Rizon et al, 2005; Meng et al, 2018). It often suffers from degradation in performance, especially in terms of speed, because of the large number of edges created by a complex background or texture. In the analysis of white blood cells, some of the current techniques used are gradient vector flow, the snake algorithm and Zack thresholding which can be used for segmenting the nuclei of cells (Sahastrabuddhe, 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
