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.
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