Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of mammalian teeth. Age is computed by adding the tooth eruption age to the count of annual incremental lines which are called tooth rings and appear in the cementum band. The number of rings is computed from an intensity (gray scale) image of the cementum band, by estimating the average ring width and then dividing the area of the cementum band by this estimate. The ring width is estimated by modelling the image by a hidden Markov random field, where intensities are assumed to be pixelwise conditionally independent and normally distributed, given a Markov random field of hidden binary labels, representing the“true scene”. To incorporate image macro-features (the long-range dependence among intensities and the quasi-periodicity in the placement of tooth rings), the label random field is defined by an energy function that depends on a parametric Gabor filter, convolved with the true scene. The filter parameter represents the unknown of main interest, i.e. the average width of the rings. The model is estimated through an EM algorithm, relying on the mean field approximation of the hidden label distribution and allows to predict the locations of the rings in the image.

Klauenberg, K., Lagona, F. (2007). Hidden Markov Random Field Models for TCA Image Analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 52, 855-868 [10.1016/j.csda.2007.06.022].

Hidden Markov Random Field Models for TCA Image Analysis

LAGONA, Francesco
2007

Abstract

Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of mammalian teeth. Age is computed by adding the tooth eruption age to the count of annual incremental lines which are called tooth rings and appear in the cementum band. The number of rings is computed from an intensity (gray scale) image of the cementum band, by estimating the average ring width and then dividing the area of the cementum band by this estimate. The ring width is estimated by modelling the image by a hidden Markov random field, where intensities are assumed to be pixelwise conditionally independent and normally distributed, given a Markov random field of hidden binary labels, representing the“true scene”. To incorporate image macro-features (the long-range dependence among intensities and the quasi-periodicity in the placement of tooth rings), the label random field is defined by an energy function that depends on a parametric Gabor filter, convolved with the true scene. The filter parameter represents the unknown of main interest, i.e. the average width of the rings. The model is estimated through an EM algorithm, relying on the mean field approximation of the hidden label distribution and allows to predict the locations of the rings in the image.
Klauenberg, K., Lagona, F. (2007). Hidden Markov Random Field Models for TCA Image Analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 52, 855-868 [10.1016/j.csda.2007.06.022].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/145605
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