@inbook {843931,
	title = {Towards automated cellular image segmentation for RNAi genome-wide screening.},
	booktitle = {Med Image Comput Comput Assist Interv},
	volume = {8},
	number = {Pt 1},
	year = {2005},
	month = {2005},
	pages = {885-92},
	abstract = {The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.},
	keywords = {Algorithms, Artificial Intelligence, Cluster Analysis, Gene Expression Profiling, Genomics, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Fluorescence, Pattern Recognition, Automated, Proteome, Reproducibility of Results, RNA Interference, Sensitivity and Specificity},
	author = {Zhou, Xiaobo and Liu, K Y and Bradley, P and Perrimon, N and Wong, Stephen T C}
}
