Automated Viral Plaque Counting Using Image Segmentation and Morphological Analysis

Manual counting of viral plaques is a tedious and labor-intensive process. In this paper, an efficient and economical method is proposed for automating viral plaque counting via image segmentation and various morphological operations. The method first segments a plate image into individual well images. Then, it converts each well image into a binary image and creates a new image by merging the dilated binary image and the complement image of the eroded binary image. At last, the contour hierarchy of the merged image is obtained and the plaque count is calculated by evaluating each outer contour count and its inner contour counts. Experiment results showed that the counting accuracy for the proposed method is up to 90 percent and the average processing time for a single image is about one second. An open source implementation with optional graphical user interface is available for public use.