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.
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Lenseigne, B., Dorval, T., Ogier, A., Genovesio, A. (2007). A New Color Representation for Intensity Independent Pixel Classification in Confocal Microscopy Images. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_54
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DOI: https://doi.org/10.1007/978-3-540-74607-2_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
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