title: An automatic integrated approach for stained neuron detection in studying neuron migration.
abstract: 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.
authors.affiliation: Biomedical Engineering Department, Medical School, Tsinghua University, Beijing 100084, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Huang
authors.forename: Xuezhi
authors.initials: X
authors.lastname: Sun
authors.forename: Guangshu
authors.initials: G
authors.lastname: Hu
mesh_terms: D000818:Animals; D001331:Automation / Q000379:methods*; D001921:Brain / Q000166:cytology*; D002465:Cell Movement; D005260:Female; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D051379:Mice; D008813:Mice, Inbred ICR; D008853:Microscopy / Q000379:methods*; D009474:Neurons / Q000502:physiology*; D011247:Pregnancy; D013194:Staining and Labeling / Q000379:methods