Two methods of random seed generation to avoid over-segmentation with stochastic watershed: application to nuclear fuel micrographs

被引：7

作者：

Cativa Tolosa, S. ^{[2
]}

Blacher, S. ^{[1
]}

Denis, A. ^{[2
]}

Marajofsky, A. ^{[2
]}

Pirard, J. -P. ^{[1
]}

Gommes, C. J. ^{[1
]}

机构：

[1] Univ Liege, Dept Chem Engn, B-4000 Liege, Belgium

[2] UA Combustibles Nucl, Comis Nacl Energia Atom, Buenos Aires, DF, Argentina

来源：

JOURNAL OF MICROSCOPY | 2009年 / 236卷 / 01期

关键词：

Grain boundaries; image analysis; stochastic methods;

D O I：

10.1111/j.1365-2818.2009.03200.x

中图分类号：

TH742 [显微镜];

学科分类号：

摘要：

A stochastic version of the watershed algorithm is obtained by choosing randomly in the image the seeds from which the watershed regions are grown. The output of the procedure is a probability density function corresponding to the probability that each pixel belongs to a boundary. In the present paper, two stochastic seed-generation processes are explored to avoid over-segmentation. The first is a non-uniform Poisson process, the density of which is optimized on the basis of opening granulometry. The second process positions the seeds randomly within disks centred on the maxima of a distance map. The two methods are applied to characterize the grain structure of nuclear fuel pellets. Estimators are proposed for the total edge length and grain number per unit area, L-A and N-A, which take advantage of the probabilistic nature of the probability density function and do not require segmentation.

引用

页码：79 / 86

页数：8