A novel surface-based geometric approach for 3D dendritic spine detection from multi-photon excitation microscopy images

Authors:
Qing Li;Xiaobo Zhou;Zhigang Deng;Matthew Baron;Merilee A. Teylan;Yong Kim;Stephen T. C. Wong
Affiliations:
The Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Houston, TX and Computer Science Department, University of Houston, TX;The Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Houston, TX;Computer Science Department, University of Houston, TX;Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, NY;Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, NY;Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, NY;The Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Houston, TX
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 4
Cited 0

A signal processing approach to fair surface design

SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Partitioning 3D Surface Meshes Using Watershed Segmentation

IEEE Transactions on Visualization and Computer Graphics
The asymptotic decider: resolving the ambiguity in marching cubes

VIS '91 Proceedings of the 2nd conference on Visualization '91
Extraction of fluorescent cell puncta by adaptive fuzzy segmentation

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Determining the relationship between the dendritic spine morphology and its functional properties is a fundamental while challenging problem in neurobiology research. In particular, how to accurately and automatically analyze meaningful structural information from a large microscopy image dataset is far away from being resolved. In this paper, we propose a novel method for the automated neuron reconstruction and spine detection from fluorescence microscopy images. After image processing, backbone of the neuron is obtained and the neuron is represented as a 3D surface. Based on the analysis of geometric features on the surface, spines are detected by a novel hybrid of two segmentation methods. Besides the automated detection of spines, our algorithm is able to extract accurate 3D structures of spines. Comparison results between our approach and the state of the art shows that our algorithm is more accurate and robust, especially for detecting and separating touching spines.