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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/62460

Title: A wavelet-based Bayesian framework for 3D object segmentation in microscopy
Author: PAN, KANGYU
KOKARAM, ANIL CHRISTOPHER
CORRIGAN, DAVID
RAMASWAMI, MANI
Sponsor: 
Name Grant Number
08/IN.1/I2112

Author's Homepage: http://people.tcd.ie/akokaram
http://people.tcd.ie/dacorrig
http://people.tcd.ie/ramaswam
Keywords: Microscopy
3-D objects
Issue Date: 2012
Citation: Kangyu Pan, David Corrigan, Jens Hillebrand, Mani Ramaswami, and Anil Kokaram, A wavelet-based Bayesian framework for 3D object segmentation in microscopy, Proceedings of SPIE, 8227, 2012, art. no. 82271O
Series/Report no.: Proceedings of SPIE
8227
Abstract: In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.
Description: PUBLISHED
URI: http://hdl.handle.net/2262/62460
Related links: http://link.aip.org/link/doi/10.1117/12.908916
Appears in Collections:Electronic & Electrical Eng (Scholarly Publications)

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