A shape analysis method to detect dendritic spine in 3D optical microscopy image

被引：0

作者：

Xu, Xiaoyin ^{[1
,2
]}

Cheng, Jie ^{[1
,3
]}

Witt, Rochelle M. ^{[4
]}

Sabatini, Bernardo L. ^{[4
]}

Wong, Stephen T. C. ^{[1
,2
]}

机构：

[1] Harvard Med Sch, Harvard Ctr Neurodegenerat & Repair, Boston, MA 02115 USA

[2] Brigham & Womens Hosp, Dept Radiol, Boston, MA 02115 USA

[3] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA

[4] Harvard Med Sch, Dept Neurobiol, Boston, MA 02115 USA

来源：

2006 3RD IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1-3 | 2006年

关键词：

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

Dendritic spines play an important role in synaptic transmission. Spines are small protrusions formed on the shafts of dendrites and they are manifest of many neurological conditions. Three dimensional images of dendrites and spines are obtained by confocal and two-photon laser scanning microscopy. We present an image processing method based on shape analysis to detect spines and separate spines from the dendrites for quantitative study. The method applies two grassfire procedures to first find the tips of spines and then reversely locate the boundary between spines and dendrites. The boundary is determined as the propagation front that undergoes the maximum change in plane area of the grassfire procedure. We use real data to demonstrate the performance of the method.

引用

页码：554 / +

页数：2

共 50 条

[31] Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy
Vicidomini, G.
Boccacci, P.
Diaspro, A.
Bertero, M.
JOURNAL OF MICROSCOPY, 2009, 234 (01) : 47 - 61
[32] Image processing software for 3D light microscopy
Clendenon, Jeffrey L.
Byars, Jason M.
Hyink, Deborah P.
NEPHRON EXPERIMENTAL NEPHROLOGY, 2006, 103 (02): : E50 - E54
[33] Indirect shape analysis for 3D shape retrieval
Liu, Zhenbao
Xie, Caili
Bu, Shuhui
Wang, Xiao
Han, Junwei
Lin, Hongwei
Zhang, Hao
COMPUTERS & GRAPHICS-UK, 2015, 46 : 110 - 116
[34] A new, general method of 3D model generation for active shape image segmentation
Lim, Seong-Jae
Udupa, Jayaram K.
Souza, Andre
Jeong, Yong-Yeon
Ho, Yo-Sung
Torigian, Drew A.
MEDICAL IMAGING 2006: IMAGE PROCESSING, PTS 1-3, 2006, 6144
[35] 3D Shape from SEM Image Using Improved Fast Marching Method
Huang, Lei
Iwahori, Yuji
Wang, Aili
Bhuyan, M. K.
ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS (ACIVS 2017), 2017, 10617 : 735 - 747
[36] Estimation of 3D shape from image orientations
Fleming, Roland W.
Holtmann-Rice, Daniel
Buelthoff, Heinrich H.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2011, 108 (51) : 20438 - 20443
[37] Divisional 3D shape reconstruction of object image
Xu, D
Xia, LZ
Yang, SZ
VISUALIZATION AND OPTIMIZATION TECHNIQUES, 2001, 4553 : 56 - 60
[38] 3D scene reconstruction method based on image optical flow feedback
Su, Yulin
International Journal of Information and Communication Technology, 2021, 19 (02) : 184 - 200
[39] Image analysis method for determining 3-D shape of coarse aggregate
Fernlund, JMR
CEMENT AND CONCRETE RESEARCH, 2005, 35 (08) : 1629 - 1637
[40] A simple method for 3D shape reconstruction
Pribanic, T
Cifrek, M
Tonkovic, S
PROCEEDINGS OF THE SECOND IASTED INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, 2004, : 151 - 156

← 1 2 3 4 5 →