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Morphological observation and analysis of cerebral microvascular network is an essential way to study cerebral function. Automated labeling of cerebral microvascular in microscopy images is one of the key steps for quantitative analysis of microvascular network in the specimens of brain mantle. It is presented in this work that an automated image processing approach based on curvilinear structure detector is applied to label and analyze the microvascular in the image. Steerable filter is also introduced to address the detecting confusion in branching regions. And then the vascular morphology analysis, such as average microvascular density, is also performed after image processing. Validation has demonstrated that the results from proposed approach are satisfied. The proposed method is finally applied in the study of cerebral microvascular dysfunction induced by γ-ray irradiation., (Copyright © 2011 Wiley Periodicals, Inc.)

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.