TY - JOUR
T1 - A novel tracing algorithm for high throughput imaging. Screening of neuron-based assays
AU - Zhang, Yong
AU - Zhou, Xiaobo
AU - Degterev, Alexei
AU - Lipinski, Marta
AU - Adjeroh, Donald
AU - Yuan, Junying
AU - Wong, Stephen T.C.
N1 - Funding Information:
The authors would like to acknowledge the collaboration with their biology collaborators in the Department of Cell Biology at Harvard Medical School. The authors also would like to thank Mrs. Baillie Yip for her kind help on validating the results. The authors would appreciate the members at HCNR CBI and the members at the Bioimage Analysis Group for their valuable advice, discussion, and facility support. This research is funded by the HCNR Center for Bioinformatics Research Grant, Harvard Medical School and a NIH R01 LM008696 Grant to S.T.C. Wong.
Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2007/2/15
Y1 - 2007/2/15
N2 - High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The method usually includes detection of neurite structures, feature extraction, quantification, and statistical analysis. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center-line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction, and robust enough for usage on images with poor quality, such as those with low contrast or low signal-to-noise ratio. It is able to completely and accurately extract neurite segments in neuron images with highly complicated neurite structures. Robustness comes from the use of 2D smoothening techniques and the idea of center-line extraction by estimating the surrounding edges. Efficiency is achieved by processing only pixels that are close enough to the line structures, and by carefully chosen stopping conditions. These make the proposed approach suitable for demanding image processing tasks in high throughput screening of neuron-based assays. Detailed results on experimental validation of the proposed method and on its comparative performance with other proposed schemes are included.
AB - High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The method usually includes detection of neurite structures, feature extraction, quantification, and statistical analysis. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center-line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction, and robust enough for usage on images with poor quality, such as those with low contrast or low signal-to-noise ratio. It is able to completely and accurately extract neurite segments in neuron images with highly complicated neurite structures. Robustness comes from the use of 2D smoothening techniques and the idea of center-line extraction by estimating the surrounding edges. Efficiency is achieved by processing only pixels that are close enough to the line structures, and by carefully chosen stopping conditions. These make the proposed approach suitable for demanding image processing tasks in high throughput screening of neuron-based assays. Detailed results on experimental validation of the proposed method and on its comparative performance with other proposed schemes are included.
KW - Automatic tracing
KW - Branching structures
KW - High throughput
KW - Neurite extraction
KW - Neuron image processing
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U2 - 10.1016/j.jneumeth.2006.07.028
DO - 10.1016/j.jneumeth.2006.07.028
M3 - Article
C2 - 16987551
AN - SCOPUS:33846443177
VL - 160
SP - 149
EP - 162
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
IS - 1
ER -