Set-permutation-occurrence matrix based texture segmentation

Reyer Zwiggelaar; Lilian Blot; David Raba; Erika R. E. Denton

doi:10.1007/978-3-540-44871-6_127

Set-permutation-occurrence matrix based texture segmentation

Reyer Zwiggelaar, Lilian Blot, David Raba, Erika R. E. Denton

School of Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter

11 Citations (Scopus)

Abstract

We have investigated a combination of statistical modelling and expectation maximisation for a texture based approach to the segmentation of mammographic images. Texture modelling is based on the implicit incorporation of spatial information through the introduction of a set-permutation-occurrence matrix. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling is evaluated. The developed segmentation results are used for automatic mammographic risk assessment.

Original language	English
Title of host publication	Pattern Recognition and Image Analysis
Publisher	Springer Berlin / Heidelberg
Pages	1099-1107
Number of pages	9
Volume	2652
DOIs	https://doi.org/10.1007/978-3-540-44871-6_127
Publication status	Published - 2003

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg

Access to Document

10.1007/978-3-540-44871-6_127

Cite this

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title = "Set-permutation-occurrence matrix based texture segmentation",

abstract = "We have investigated a combination of statistical modelling and expectation maximisation for a texture based approach to the segmentation of mammographic images. Texture modelling is based on the implicit incorporation of spatial information through the introduction of a set-permutation-occurrence matrix. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling is evaluated. The developed segmentation results are used for automatic mammographic risk assessment.",

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AU - Raba, David

AU - Denton, Erika R. E.

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AB - We have investigated a combination of statistical modelling and expectation maximisation for a texture based approach to the segmentation of mammographic images. Texture modelling is based on the implicit incorporation of spatial information through the introduction of a set-permutation-occurrence matrix. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling is evaluated. The developed segmentation results are used for automatic mammographic risk assessment.

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