10 November 2010 Automated centreline extraction of neuronal dendrite from optical microscopy image stacks
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Abstract
In this work we present a novel vision-based pipeline for automated skeleton detection and centreline extraction of neuronal dendrite from optical microscopy image stacks. The proposed pipeline is an integrated solution that merges image stacks pre-processing, the seed points detection, ridge traversal procedure, minimum spanning tree optimization and tree trimming into to a unified framework to deal with the challenge problem. In image stacks preprocessing, we first apply a curvelet transform based shrinkage and cycle spinning technique to remove the noise. This is followed by the adaptive threshold method to compute the result of neuronal object segmentation, and the 3D distance transformation is performed to get the distance map. According to the eigenvalues and eigenvectors of the Hessian matrix, the skeleton seed points are detected. Staring from the seed points, the initial centrelines are obtained using ridge traversal procedure. After that, we use minimum spanning tree to organize the geometrical structure of the skeleton points, and then we use graph trimming post-processing to compute the final centreline. Experimental results on different datasets demonstrate that our approach has high reliability, good robustness and requires less user interaction.
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Liang Xiao, Liang Xiao, Fanbiao Zhang, Fanbiao Zhang, } "Automated centreline extraction of neuronal dendrite from optical microscopy image stacks", Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 78502Q (10 November 2010); doi: 10.1117/12.871721; https://doi.org/10.1117/12.871721
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