Paper Info
Title
Human Embryonic Stem Cell Detection by Spatial Information and Mixture of Gaussians
Abstract
Human Embryonic Stem Cells (HESCs) possess the potential to provide treatments for cancer, Parkinson's disease, Huntington's disease, Type 1 diabetes mellitus etc. Consequently, HESCs are often used in the biological assay to study the effects of chemical agents in the human body. However, detection of HESC is often a challenge in phase contrast images. To improve the accuracy of HESC colony detection, we combine spatial information and the outcome of a mixture of Gaussians model. While a mixture of Gaussians generates reasonable labels for various regions of HESC images, it lacks spatial details and connectivity. Sets of spatially consistent candidate labeling are generated by median filtering the image at different scales followed by thresholding. An optimal combination of filter scale and threshold which maximizes the correlation coefficient between the spatial information and the mixture of Gaussians output is obtained. The paper validates the method for various HESC videos.
Year
DOI
Venue
2011
10.1109/HISB.2011.30
HISB
Keywords
Field
DocType
spatial detail,various hesc video,biological assay,hesc image,gaussians output,spatial information,human embryonic stem cells,gaussians model,hesc colony detection,various region,attenuation,median filtering,entropy,gaussian mixture model,correlation,stem cells,expectation maximization algorithm,apoptosis,mixture of gaussians
Phase contrast microscopy,Spatial analysis,Computer vision,Cellular biophysics,Median filter,Pattern recognition,Computer science,Expectation–maximization algorithm,Embryonic stem cell,Artificial intelligence,Thresholding,Mixture model
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
5
Name
Order
Citations
PageRank
Benjamin Xueqi Guan122.06
Bir Bhanu23356380.19
Ninad Thakoor39413.39
Prudence Talbot451.87
Sabrina Lin541.53