Analysis of cancer cell morphology in fluorescence microscopy image exploiting shape descriptor

Cancer cell morphology is closely related to their phenotype and activity. These characteristics are important in drug-response prediction for personalized cancer therapeutics. We used multi-channel fluorescence microscopy images to analyze the morphology of highly cohesive cancer cells. First, we detected individual nuclei regions in single-channel images using advanced simple linear iterative clustering. The center points of the nuclei regions were used as seeds for the Voronoi diagram method to extract spatial arrangement features from cell images. Human cancer cell populations form irregularly shaped aggregates, making their detection more difficult. We overcame this problem by identifying individual cells using an image-based shape descriptor. Finally, we analyzed the correlation between cell agglutination and cell shape.