DocumentCode
419806
Title
Automatic color space selection for biological image segmentation
Author
Meas-Yedid, V. ; Glory, E. ; Morelon, E. ; Pinset, Ch. ; Stamon, G. ; Olivo-Marin, J.-Ch.
Author_Institution
Quantitative Image Anal. Unit, Celogos Inst. Pasteur, Paris, France
Volume
3
fYear
2004
fDate
23-26 Aug. 2004
Firstpage
514
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.
Keywords
image colour analysis; image segmentation; medical image processing; Borsotti criteria; Liu criteria; automatic color space selection; biological image segmentation; color segmentation method; Automatic testing; Colored noise; Euclidean distance; Humans; Image color analysis; Image segmentation; Microscopy; Pattern recognition; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-2128-2
Type
conf
DOI
10.1109/ICPR.2004.1334579
Filename
1334579
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