An automated approach for cerebral microvascularity labeling in microscopy images

Quantitative morphological analysis of curvilinear network for microscopic image based on individual fibre segmentation (IFS)

Microscopic images of curvilinear fibre network structure like cytoskeleton are traditionally analysed by qualitative observation, which can hardly provide quantitative information of their morphological properties. However, such information is crucially contributive to the understanding of important biological events, even helps to learn about the inner relations hard to perceive. Individual fibre segmentation-based curvilinear structure detector proposed in this study can identify each individual fibre in the network, as well as connections between different fibres. Quantitative information of each individual fibre, including length, orientation and position, can be extracted; so are the connecting modes in the fibre network, such as bifurcation, intersection and overlap. Distribution of fibres with different morphological properties is also presented. No manual intervening or subjective judging is required in the analysing process. Both synthesized and experimental microscopic images have verified that the detector is capable to segment curvilinear network at the subcellular level with strong noise immunity. The proposed detector is finally applied to the morphological study on cytoskeleton. It is believed that the individual fibre segmentation-based curvilinear structure detector can greatly enhance our understanding of those biological images generated from tons of biological experiments.