Stochastic labelling of biological images |
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Authors: | G. Ayala,& A. Simó |
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Affiliation: | Dpto. de Estadística e I.O. e Instituto de Robótica de la Universitat de Valencia, Dr. Moliner 50, 46100-Burjasot, Spain,;Dpto. de Matemáticas de la Universitat Jaume I, Campus Penyeta Roja, Csatellón, Spain |
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Abstract: | Many hypotheses made by experimental researchers can be formulated as a stochastic labelling of a given image. Some stochastic labelling methods for random closed sets are proposed in this paper. Molchanov (I. Molchanov, 1984, Theor. Probability and Math. Statist. 29 , 113–119) provided the probabilistic background for this problem. However, there is a lack of specific labelling models. Ayala and Simó (G. Ayala and A. Simó, 1995, Advances in Applied Probability 27 , 293–305) proposed a method in which, given the whole set of connected components, every component is classified in a certain phase or category in a completely random way. Alternative methods are necessary in case the random labelling hypothesis is not reliable. A different kind of labelling method is proposed that considers the environment: the type of every connected component is a function of its location. Two different biphase images are studied: a cross section of a nerve from a rat, and a cross section of an optic nerve from a lizard. |
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Keywords: | bivariate random closed set random labelling K functions randomization test |
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