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Proceedings Paper

A novel approach for three dimensional dendrite spine segmentation and classification
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Paper Abstract

Dendritic spines are small, bulbous cellular compartments that carry synapses. Biologists have been studying the biochemical and genetic pathways by examining the morphological changes of the dendritic spines at the intracellular level. Automatic dendritic spine detection from high resolution microscopic images is an important step for such morphological studies. In this paper, a novel approach to automated dendritic spine detection is proposed based on a nonlinear degeneration model. Dendritic spines are recognized as small objects with variable shapes attached to dendritic backbones. We explore the problem of dendritic spine detection from a different angle, i.e., the nonlinear degeneration equation (NDE) is utilized to enhance the morphological differences between the dendrite and spines. Using NDE, we simulated degeneration for dendritic spine detection. Based on the morphological features, the shrinking rate on dendrite pixels is different from that on spines, so that spines can be detected and segmented after degeneration simulation. Then, to separate spines into different types, Gaussian curvatures were employed, and the biomimetic pattern recognition theory was applied for spine classification. In the experiments, we compared quantitatively the spine detection accuracy with previous methods, and the results showed the accuracy and superiority of our methods.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831437 (14 February 2012); doi: 10.1117/12.911693
Show Author Affiliations
Tiancheng He, Methodist Hospital Research Institute (United States)
Zhong Xue, Methodist Hospital Research Institute (United States)
Stephen T. C. Wong, Methodist Hospital Research Institute (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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