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Stochastic labelling of biological images
Author(s) -
Ayala G.,
Simó A.
Publication year - 1998
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.00074
Subject(s) - labelling , probabilistic logic , set (abstract data type) , component (thermodynamics) , mathematics , image (mathematics) , stochastic process , computer science , algorithm , artificial intelligence , statistics , physics , criminology , sociology , thermodynamics , programming language
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.