GRAPH CUT BASED ACTIVE CONTOUR FOR AUTOMATED CELLULAR IMAGE SEGMENTATION IN HIGH THROUGHPUT RNA INTERFACE (RNAi) SCREENING

Recently, image-based, high throughput RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Effective automated segmentation technique is significant in analysis of RNAi images. However, graph cuts based active contour (GCBAC) method needs interaction during segmentation. Here, we present a novel approach to overcome this shortcoming. The process consists the following steps: First, region-growing algorithm uses extracted nuclei to get the initial contours for segmentation of cytoplasm. Then, constraint factor obtained from binary segmentation of enhanced image is incorporated to improve the performance of cytoplasm segmentation. Finally, morphological thinning algorithm is implemented to solve the touching problem of clustered cells. Our approach can automatically segment clustered cells with polynomial time-consuming. The excellent results verify the effectiveness of the proposed approach