This work presents an imageJ plugin developed for molecular biologists who works on yeast images stained with fluorescence dyes, and interested in extracting some features of the yeast cells under study, to identify whether a mutation of these genes leads to changes in some of the characteristics of the cells. For different set of images, users can choose different segmentation methods which are based on noise removal algorithms, edge detection filters, blurring filters, thresholding and watershed algorithm. It extracts a number of features for the yeast cells under study, such as area, intensity, perimeter, density, vacuole size, and some other textures based on intensity histogram; these features are output into a csv file, as well as the outlines and overlays of cells and overlays of nuclei if presented.
imageJ Plugin, Features Extraction, Segmentation, Yeast, Fluorescence dyes
Phd. student at Leiden Institute of Advanced Computer Science (Leiden University)- Section Imaging and Bioinformatics of the Imagery and Media department; My current research is focused on image analysis and features extraction of yeast-cell images.
Presenting author: Mohamed Tleis
Organisation: LIACS, Leiden University
co-authors: Fons Verbeek