Quantification of phenotypes in high-content screening experiments depends on the accuracy of single cell analysis. In such
analysis workflows, cell nuclei segmentation is typically the first step and is followed by cell body segmentation, feature
extraction, and subsequent data analysis workflows. Therefore, it is of utmost importance that the first steps of high-content
analysis are done accurately in order to guarantee correctness of the final analysis results. In this paper, we present a novel
cell nuclei image segmentation framework which exploits robustness of graph cut to obtain initial segmentation for image
intensity-based clump splitting method to deliver the accurate overall segmentation. By using quantitative benchmarks and
qualitative comparison with real images from high-content screening experiments with complicated multinucleate cells, we
show that our method outperforms other state-of-the-art nuclei segmentation methods. Moreover, we provide a modular
and easy-to-use implementation of the method for a widely used platform.
Addressing spots in microarray images and deriving expression values for corresponding genes are fundamental tasks in microarray image analysis. Reliable expression values can be obtained only if the spot locations are accurately known. Here, a novel approach
for spot addressing in microarray images based on supervised learning is proposed. The aim is to locate each spot through classifying the image based on local features into spot centers and background using support vector machine classifier. The resulting spot location information is complemented through image processing methods in the post-processing phase. Our method, through searching locations for individual spots, enables accurate segmentation and extraction of expression values. The benefit of searching individual spots becomes clear in case of misaligned spots or spot rows.