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

Qing Li, Xiaobo Zhou, Zhigang Deng, Matthew Baron, Merilee A. Teylan, Yong Kim, Stephen T.C. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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 ourapproach and the state of the art shows that our algorithm is more accurate and robust, especially for detecting and separating touching spines. Index Terms - Spine detection, geometric measurement estimation, watershed, microscopy imagesDetermining 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 fluorescencemicroscopy 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 thesurface, spines are detected by a novel hybrid of two segmentation methods. Besides the automated detection of spines, our algorithm is able to extract accurate 3Dstructures 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.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages1255-1258
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period6/28/097/1/09

Keywords

  • Geometric measurement estimation
  • Microscopy images
  • Spine detection
  • Watershed

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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