A new color representation for intensity independent pixel classification in confocal microscopy images

Authors:
Boris Lenseigne;Thierry Dorval;Arnaud Ogier;Auguste Genovesio
Affiliations:
Image Mining Group, Institut Pasteur Korea, Seoul Korea;Image Mining Group, Institut Pasteur Korea, Seoul Korea;Image Mining Group, Institut Pasteur Korea, Seoul Korea;Image Mining Group, Institut Pasteur Korea, Seoul Korea
Venue:
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Year:
2007

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Abstract

We address the problem of pixel classification in fluorescence microscopy images by only using wavelength information. To achieve this, we use Support Vector Machines as supervised classifiers and pixels components as feature vectors. We propose a representation derived from the HSV color space that allows separation between color and intensity information. An extension of this transformation is also presented that allows to performs an a priori object/background segmentation. We show that these transformations not only allows intensity independent classification but also makes the classification problem more simple. As an illustration, we perform intensity independent pixel classification first on a synthetic then on real biological images.