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A generalized Potts model for confocal microscopy images

Much as being among the least invasive mainstream imaging technologies in life sciences, the resolution of confocal microscopy is limited. Imaged structures, e.g., chromatin-fiber loops, have diameters around or beyond the diffraction limit, and microscopy images show seemingly random spatial densit...

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Hauptverfasser: Máté, Gabriell (VerfasserIn) , Heermann, Dieter W. (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 26 January 2015
In: International journal of modern physics. B, Condensed matter physics etc.
Year: 2015, Jahrgang: 29, Heft: 08, Pages: 1550048
ISSN:1793-6578
DOI:10.1142/S0217979215500484
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1142/S0217979215500484
Verlag, Volltext: http://www.worldscientific.com/doi/abs/10.1142/S0217979215500484
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Verfasserangaben:Gabriell Máté, Dieter W. Heermann

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520 |a Much as being among the least invasive mainstream imaging technologies in life sciences, the resolution of confocal microscopy is limited. Imaged structures, e.g., chromatin-fiber loops, have diameters around or beyond the diffraction limit, and microscopy images show seemingly random spatial density distributions only. While such images are important because the organization of the chromosomes influences different cell mechanisms, many interesting questions can also be related to the observed patterns. These concern their spatial aspects, the role of randomness, the possibility of modeling these images with a random generative process, the interaction between the densities of adjacent loci, the length-scales of these influences, etc. We answer these questions by implementing a generalization of the Potts model. We show how to estimate the model parameters, test the performance of the estimation process and numerically prove that the obtained values converge to the ground truth. Finally, we generate images with a trained model and show that they compare well to real cell images. 
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