TY - GEN
T1 - A novel surface-based geometric approach for 3d dendritic spine detection from multi-photon excitation microscopy images
AU - Li, Qing
AU - Zhou, Xiaobo
AU - Deng, Zhigang
AU - Baron, Matthew
AU - Teylan, Merilee A.
AU - Kim, Yong
AU - Wong, Stephen T.C.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Geometric measurement estimation
KW - Microscopy images
KW - Spine detection
KW - Watershed
UR - http://www.scopus.com/inward/record.url?scp=70449336056&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449336056&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2009.5193290
DO - 10.1109/ISBI.2009.5193290
M3 - Conference contribution
AN - SCOPUS:70449336056
SN - 9781424439324
T3 - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
SP - 1255
EP - 1258
BT - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging
T2 - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Y2 - 28 June 2009 through 1 July 2009
ER -