Title of article :
Coupled segmentation of nuclear and membrane-bound macromolecules through voting and multiphase level set
Author/Authors :
Chang، نويسنده , , Hang and Wen، نويسنده , , Quan and Parvin، نويسنده , , Bahram، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2015
Pages :
12
From page :
882
To page :
893
Abstract :
Membrane-bound macromolecules play an important role in tissue architecture and cell–cell communication, and is regulated by almost one-third of the genome. At the optical scale, one group of membrane proteins expresses themselves as linear structures along the cell surface boundaries, while others are sequestered; and this paper targets the former group. Segmentation of these membrane proteins on a cell-by-cell basis enables the quantitative assessment of localization for comparative analysis. However, such membrane proteins typically lack continuity, and their intensity distributions are often very heterogeneous; moreover, nuclei can form large clump, which further impedes the quantification of membrane signals on a cell-by-cell basis. To tackle these problems, we introduce a three-step process to (i) regularize the membrane signal through iterative tangential voting, (ii) constrain the location of surface proteins by nuclear features, where clumps of nuclei are segmented through a delaunay triangulation approach, and (iii) assign membrane-bound macromolecules to individual cells through an application of multi-phase geodesic level-set. We have validated our method using both synthetic data and a dataset of 200 images, and are able to demonstrate the efficacy of our approach with superior performance. mentary Information: One online video example can be found with: http://vision.lbl.gov/People/hang/evolving_fronts.gif
Keywords :
Multi-phase level set , Nuclear segmentation , Tissue architecture , Segmentation of membrane-bound macromolecules , Perceptual grouping
Journal title :
PATTERN RECOGNITION
Serial Year :
2015
Journal title :
PATTERN RECOGNITION
Record number :
1879972
Link To Document :
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