Efficient processing of fluorescence images using directional multiscale representations

David Damanik, Michael Ruzhansky, Vitali Vougalter, M. W. Wong, D. Labate, Fernanda Laezza, P. Negi, B. Ozcan, M. Papadakis

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.

Original languageEnglish (US)
Pages (from-to)177-193
Number of pages17
JournalMathematical Modelling of Natural Phenomena
Volume9
Issue number5
DOIs
StatePublished - 2014

Fingerprint

Image Analysis
Fluorescence
Image analysis
Neurons
Neuron
Fluorescence Microscopy
Confocal
Signaling Pathways
Fluorescence microscopy
Processing
Quantification
Brain
Data analysis
High Resolution
Limiting
Cells
Tissue
Perturbation
Cell
Range of data

Keywords

  • Curvelets
  • Fluorescent microscopy
  • Image processing
  • Segmentation
  • Shearlets
  • Sparse representations
  • Wavelets

ASJC Scopus subject areas

  • Modeling and Simulation

Cite this

Efficient processing of fluorescence images using directional multiscale representations. / Damanik, David; Ruzhansky, Michael; Vougalter, Vitali; Wong, M. W.; Labate, D.; Laezza, Fernanda; Negi, P.; Ozcan, B.; Papadakis, M.

In: Mathematical Modelling of Natural Phenomena, Vol. 9, No. 5, 2014, p. 177-193.

Research output: Contribution to journalArticle

Damanik, D, Ruzhansky, M, Vougalter, V, Wong, MW, Labate, D, Laezza, F, Negi, P, Ozcan, B & Papadakis, M 2014, 'Efficient processing of fluorescence images using directional multiscale representations', Mathematical Modelling of Natural Phenomena, vol. 9, no. 5, pp. 177-193. https://doi.org/10.1051/mmnp/20149512
Damanik, David ; Ruzhansky, Michael ; Vougalter, Vitali ; Wong, M. W. ; Labate, D. ; Laezza, Fernanda ; Negi, P. ; Ozcan, B. ; Papadakis, M. / Efficient processing of fluorescence images using directional multiscale representations. In: Mathematical Modelling of Natural Phenomena. 2014 ; Vol. 9, No. 5. pp. 177-193.
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