A wavelet-based Bayesian framework for 3D object segmentation in microscopy
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2012Citation:
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. 82271ODownload Item:
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.
Sponsor
Grant Number
Science Foundation Ireland (SFI)
08/IN.1/I2112
Author's Homepage:
http://people.tcd.ie/akokaramhttp://people.tcd.ie/ramaswam
http://people.tcd.ie/dacorrig
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Journal ArticleSeries/Report no:
Proceedings of SPIE8227
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Microscopy, 3-D objectsSubject (TCD):
Nanoscience & MaterialsMetadata
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