Automated cell individualization and counting in cerebral microscopic images - Molecular Imaging Research Center Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Automated cell individualization and counting in cerebral microscopic images

Résumé

In biomedical research, cell counting is important to assess physiological and pathophysiological information. However, the automated analysis of microscopic images of tissues remains extremely challenging. We propose an automated processing protocol for proper segmentation of individual cells in microscopic images. A Gaussian filter is applied to improve signal to noise ratio (SNR) then an original minmax method is proposed to produce an image in which information describing both cell centers (minima) and boundaries are enhanced. Finally, a contour-based model initialized from minima in the min-max cartography is carried out to achieve cell individualization. This method is evaluated on a NeuN-stained macaque brain section in sub-regions presenting various levels of fraction of neuron surface occupation. Comparison with several methods of reference demonstrates that the performances of our method are superior. A first application to the segmentation of neurons in the hippocampus illustrates the ability of our approach to deal with massive and complex data.
Fichier principal
Vignette du fichier
you_version_auteur.pdf (658.08 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01539966 , version 1 (06-07-2017)

Identifiants

Citer

Zhenzhen You, Michel Vandenberghe, Yael Balbastre, Nicolas Souedet, Philippe Hantraye, et al.. Automated cell individualization and counting in cerebral microscopic images. 2016 IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, United States. ⟨10.1109/ICIP.2016.7532988⟩. ⟨hal-01539966⟩
244 Consultations
158 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More