Automatic and reliable extraction of dendrite backbone from optical microscopy images

Authors:
Liang Xiao;Xiaosong Yuan;Zack Galbreath;Badrinath Roysam
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China and Department of Electrical, Computer & Systems Engineering, Rensselaer Polytechni ...;Department of Electrical, Computer & Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY;Department of Electrical, Computer & Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY;Department of Electrical, Computer & Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Year:
2010

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Abstract

The morphology and structure of 3D dendritic backbones are the essential to understand the neuronal circuitry and behaviors in the neurodegenerative diseases. As a big challenge, the research of extraction of dendritic backbones using image processing and analysis technology has attracted many computational scientists. This paper proposes a reliable and robust approach for automatically extract dendritic backbones in 3D optical microscopy images. Our systematic scheme is a gradient vector field based skeletonization approach. We first use self-snake based nonlinear diffusion, adaptive segmentation to smooth noise and segment the neuron object. Then we propose a hierarchical skeleton points detection algorithm (HSPD) using the measurement criteria of low divergence and high iso-surface principle curvature. We further create a minimum spanning tree to represent and establish effective connections among skeleton points and prune small and spurious branches. To improve the robustness and reliability, the dendrite backbones are refined by B-Spline kernel based data fitting. Experimental results on different datasets demonstrate that our approach has high reliability, good robustness and requires less user interaction.