A surface based approach for dendritic spine detection

Neuron reconstruction and dendritic spine identification on a large data set of microscopy images is essential for understanding the relationship between morphology and functions of dendritic spines.Dendrites are the tree-like structures of neuronal cells, and spines are small protrusions on the surface of dendrites. Spines have various visual shapes (e.g., mushroom, thin, and stubby) and can appear or disappear over time. Existing neurobiology literature shows that the morphological changes of spines and the dendritic spine structures are highly correlated with their underlying cognitive functions.How to accurately and automatically analyse meaningful structural information from a large microscopy image data set is a difficult task. One challenge in spine detection and segmentation is how to automatically separate touching spines. In this paper, based on various global and local geometric features of the dendrite structure touching spines are detected and to segment them a breaking-down and stitching-up algorithm is used.