Classify cellular phenotype in high-throughput fluorescence microcopy images for RNAi genome-wide screening

Jun Wang, Xiaobo Zhou, Fuhai Li, Stephen T.C. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs
StatePublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States
Duration: Jul 13 2006Jul 14 2006

Publication series

Name2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006

Conference

Conference2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
CountryUnited States
CityBethesda, MD
Period07/13/0607/14/06

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