Automatic Color Space Selection for Biological Image Segmentation

Authors:
V. Meas-Yedid;E. Glory;E. Morelon;Ch. Pinset;G. Stamon;J-Ch. Olivo-Marin
Affiliations:
Quantitative Image Analysis Unit, France;Quantitative Image Analysis Unit, France/ Celogos Institut Pasteur, France/ SIP-CRIP5, Université/ Paris5, France;Hô/pital Necker, France;Celogos Institut Pasteur, France;SIP-CRIP5, Université/ Paris5, France;Quantitative Image Analysis Unit, France
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Year:
2004

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Abstract

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.