Automatic and Reliable Extraction of Dendrite Backbone from Optical Microscopy Images

• Published in: Life System Modeling and Intelligent Computing pp. 100 - 112
• Main Authors: Xiao, Liang, Yuan, Xiaosong, Galbreath, Zack, Roysam, Badrinath
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Curve-skeleton; Dendrite backbone; Neuron; Reconstruction
• ISBN: 3642156142; 9783642156144
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-15615-1_13

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.