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Deconvolving Active Contours for Fluorescence Microscopy Images


Helmuth, Jo A; Sbalzarini, Ivo F (2009). Deconvolving Active Contours for Fluorescence Microscopy Images. Lecture Notes in Computer Science, 5875:544-553.

Abstract

We extend active contours to constrained iterative deconvolution by replacing the external energy function with a model-based likelihood. This enables sub-pixel estimation of the outlines of diffractionlimited objects, such as intracellular structures, from fluorescence micrographs. We present an efficient algorithm for solving the resulting optimization problem and robustly estimate object outlines. We benchmark the algorithm on artificial images and assess its practical utility on fluorescence micrographs of the Golgi and endosomes in live cells.

We extend active contours to constrained iterative deconvolution by replacing the external energy function with a model-based likelihood. This enables sub-pixel estimation of the outlines of diffractionlimited objects, such as intracellular structures, from fluorescence micrographs. We present an efficient algorithm for solving the resulting optimization problem and robustly estimate object outlines. We benchmark the algorithm on artificial images and assess its practical utility on fluorescence micrographs of the Golgi and endosomes in live cells.

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8 citations in Web of Science®
2 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > LipidX
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2009
Deposited On:05 Jul 2013 11:33
Last Modified:05 Apr 2016 16:51
Publisher:Springer
ISSN:0302-9743
Publisher DOI:https://doi.org/10.1007/978-3-642-10331-5_51
Permanent URL: https://doi.org/10.5167/uzh-79212

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