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An automatic integrated approach for stained neuron detection in studying neuron migration
Author(s) -
Huang Yue,
Sun Xuezhi,
Hu Guangshu
Publication year - 2010
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.20762
Subject(s) - computer science , artificial intelligence , thresholding , pattern recognition (psychology) , neuron , neuroscience , constraint (computer aided design) , fuzzy logic , cluster analysis , computer vision , image (mathematics) , biology , mathematics , geometry
Neurons that come to populate the six‐layered cerebral cortex are born deep within the developing brain in the surface of the embryonic cerebral ventricles. It is very important to detect these neurons for studying histogenesis of the brain and abnormal migration that had been linked to cognitive deficits, mental retardation, and motor disorders. The visualization of labeled cells in brain sections was performed by immunocytochemical examination and its image data were documented to microscopic pictures. Based on the fact, automatic accurate neurons labeling is prerequisite instead of time‐consuming manual labeling. In this article, a fully automated image processing approach is proposed to detect all the stained neurons in microscopic images. First of all, dark stained neurons are achieved by thresholding in blue channel of image. And then a modified fuzzy c‐means clustering method, called alternative fuzzy c‐means is applied to achieve higher classification accuracy in extracting constraint factor. Finally, watershed based on gradient vector flow is employed to the constraint factor image to segment all the neurons, including clustered neurons. The results demonstrate that the proposed method can be a useful tool in neuron image analysis. Microsc. Res. Tech. 2010. © 2009 Wiley‐Liss, Inc.