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Proceedings Paper

A wavelet-based Bayesian framework for 3D object segmentation in microscopy
Author(s): Kangyu Pan; David Corrigan; Jens Hillebrand; Mani Ramaswami; Anil Kokaram
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Paper 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.

Paper Details

Date Published: 2 February 2012
PDF: 15 pages
Proc. SPIE 8227, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIX, 82271O (2 February 2012); doi: 10.1117/12.908916
Show Author Affiliations
Kangyu Pan, Trinity College Dublin (Ireland)
David Corrigan, Trinity College Dublin (Ireland)
Jens Hillebrand, Trinity College Dublin (Ireland)
Mani Ramaswami, Trinity College Dublin (Ireland)
Anil Kokaram, Trinity College Dublin (Ireland)


Published in SPIE Proceedings Vol. 8227:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIX
Jose-Angel Conchello; Carol J. Cogswell; Tony Wilson; Thomas G. Brown, Editor(s)

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