Automated Cell Phase Classification for Zebrafish Fluorescence Microscope Images

Automated cell phenotype image classification is an interesting bioinformatics problem. In this paper, an automated cell phase classification framework is investigated for zebra fish presomitic mesoderm (PSM) images. Low image resolution, gradual transitions between adjacent categories and irregularity of real cell images make this classification task tough but intriguing. The proposed framework first segments zebra fish image into cell patches by a two-stage segmentation procedure, then extracts feature set NF9, which designed especially for this low resolution image set, on each cell patch, and finally employs support vector machine (SVM) as cell classifier. At present, the total accuracy by NF9 is 75%.