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Efficient Processing of Fluorescence Images Using DirectionalMultiscale Representations

Published online by Cambridge University Press:  17 July 2014

D. Labate,
F. Laezza,
P. Negi,
B. Ozcan and
M. Papadakis
D. Labate*
Affiliation:
Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
F. Laezza
Affiliation:
Dept. of Pharmacology and Toxicology, UT Medical Branch, Galveston, TX 77555, USA
P. Negi
Affiliation:
Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
B. Ozcan
Affiliation:
Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
M. Papadakis
Affiliation:
Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
*
⋆Corresponding author. E-mail: dlabate@math.uh.edu

Abstract

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Recent advances in high-resolution fluorescence microscopy have enabled the systematicstudy of morphological changes in large populations of cells induced by chemical andgenetic perturbations, facilitating the discovery of signaling pathways underlyingdiseases and the development of new pharmacological treatments. In these studies, though,due to the complexity of the data, quantification and analysis of morphological featuresare for the vast majority handled manually, slowing significantly data processing andlimiting often the information gained to a descriptive level. Thus, there is an urgentneed for developing highly efficient automated analysis and processing tools forfluorescent images. In this paper, we present the application of a method based on theshearlet representation for confocal image analysis of neurons. The shearletrepresentation is a newly emerged method designed to combine multiscale data analysis withsuperior directional sensitivity, making this approach particularly effective for therepresentation of objects defined over a wide range of scales and with highly anisotropicfeatures. Here, we apply the shearlet representation to problems of soma detection ofneurons in culture and extraction of geometrical features of neuronal processes in braintissue, and propose it as a new framework for large-scale fluorescent image analysis ofbiomedical data.

Keywords

curveletsfluorescent microscopyimage processingsegmentationshearletssparse representationswavelets

Information

Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 9 , Issue 5: Spectral problems , 2014, pp. 177 - 193
Copyright
© EDP Sciences, 2014

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