TY - GEN
T1 - Classify cellular phenotype in high-throughput fluorescence microcopy images for RNAi genome-wide screening
AU - Wang, Jun
AU - Zhou, Xiaobo
AU - Li, Fuhai
AU - Wong, Stephen T.C.
PY - 2006
Y1 - 2006
N2 - As we know, the genes could cause the cell phenotypes changed dramatically. Currently, biologists attempt to perform the genome-wide RNAi screening to identify various image phenotypes. It is a challenging task to recognize the phenotypes automatically because of the noisy background and low contrast of fluorescence images. In this work, we applied two cellular segmentation techniques, deformable model and Cellprofiler software, for the preprocess of cellular segmentation. Then five kinds of features including wavelet feature, moments feature, haralick co-occurrence feature, region property feature, and problem-specific shape descriptor are extracted from the cellular patches. The Genetic Algorithm (GA) is applied to select a subset of the most discriminate features to remove the irrelevance and redundancy. We use Linear Discriminant Analysis (LDA) as the tool for training the statistical classification model. Experimental results show the proposed approach works well in RNAi screening.
AB - As we know, the genes could cause the cell phenotypes changed dramatically. Currently, biologists attempt to perform the genome-wide RNAi screening to identify various image phenotypes. It is a challenging task to recognize the phenotypes automatically because of the noisy background and low contrast of fluorescence images. In this work, we applied two cellular segmentation techniques, deformable model and Cellprofiler software, for the preprocess of cellular segmentation. Then five kinds of features including wavelet feature, moments feature, haralick co-occurrence feature, region property feature, and problem-specific shape descriptor are extracted from the cellular patches. The Genetic Algorithm (GA) is applied to select a subset of the most discriminate features to remove the irrelevance and redundancy. We use Linear Discriminant Analysis (LDA) as the tool for training the statistical classification model. Experimental results show the proposed approach works well in RNAi screening.
UR - http://www.scopus.com/inward/record.url?scp=42749105411&partnerID=8YFLogxK
U2 - 10.1109/LSSA.2006.250404
DO - 10.1109/LSSA.2006.250404
M3 - Conference contribution
AN - SCOPUS:42749105411
SN - 1424402786
SN - 9781424402786
T3 - 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
BT - 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
T2 - 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
Y2 - 13 July 2006 through 14 July 2006
ER -