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17th International Conference on Pattern Recognition (ICPR'04) - Volume 3
Automatic Color Space Selection for Biological Image Segmentation
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
ASCII Text
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V. Meas-Yedid, E. Glory, E. Morelon, Ch. Pinset, G. Stamon, J-Ch. Olivo-Marin, "Automatic Color Space Selection for Biological Image Segmentation," Pattern Recognition, International Conference on, vol. 3, pp. 514-517, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 3, 2004.
 
 
BibTex
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@article{ 10.1109/ICPR.2004.1334579,
author = {V. Meas-Yedid and E. Glory and E. Morelon and Ch. Pinset and G. Stamon and J-Ch. Olivo-Marin},
title = {Automatic Color Space Selection for Biological Image Segmentation},
journal ={Pattern Recognition, International Conference on},
volume = {3},
year = {2004},
issn = {1051-4651},
pages = {514-517},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334579},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Automatic Color Space Selection for Biological Image Segmentation
SN - 1051-4651
SP514
EP517
A1 - V. Meas-Yedid,
A1 - E. Glory,
A1 - E. Morelon,
A1 - Ch. Pinset,
A1 - G. Stamon,
A1 - J-Ch. Olivo-Marin,
PY - 2004
KW - null
VL - 3
JA - Pattern Recognition, International Conference on
ER -
 
V. Meas-Yedid, Quantitative Image Analysis Unit, France
E. Glory, Quantitative Image Analysis Unit, France; Celogos Institut Pasteur, France; SIP-CRIP5, Universit? Paris5, France
E. Morelon, H?pital Necker, France
Ch. Pinset, Celogos Institut Pasteur, France
G. Stamon, SIP-CRIP5, Universit? Paris5, France
J-Ch. Olivo-Marin, Quantitative Image Analysis Unit, France
In this paper, we have tested criteria designed by Liu and Borsotti to automatically evaluate the quality of a color segmentation. As they do not correctly answer our microscopy image problems, we propose two modified criteria adapted to two different biological applications. Penalizing inhomogeneity, numerous small regions and misclassified regions, our modified criteria help to select the best color space, for a given segmentation method.
Citation:
V. Meas-Yedid, E. Glory, E. Morelon, Ch. Pinset, G. Stamon, J-Ch. Olivo-Marin, "Automatic Color Space Selection for Biological Image Segmentation," icpr, vol. 3, pp.514-517, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 3, 2004
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