A wavelet-based Bayesian framework for 3D object segmentation in microscopy

Kangyu Pan; David Corrigan; Jens Hillebrand; Mani Ramaswami; Anil Kokaram

doi:10.1117/12.908916

2 February 2012 A wavelet-based Bayesian framework for 3D object segmentation in microscopy

Kangyu Pan, David Corrigan, Jens Hillebrand, Mani Ramaswami, Anil Kokaram

Proceedings Volume 8227, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIX; 82271O (2012) https://doi.org/10.1117/12.908916
Event: SPIE BiOS, 2012, San Francisco, California, United States

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.

Citation Download Citation

Kangyu Pan, David Corrigan, Jens Hillebrand, Mani Ramaswami, and Anil Kokaram "A wavelet-based Bayesian framework for 3D object segmentation in microscopy", Proc. SPIE 8227, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIX, 82271O (2 February 2012); https://doi.org/10.1117/12.908916

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