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
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;
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Print_ISBN :
0-7695-2128-2
DOI :
10.1109/ICPR.2004.1334579
