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

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	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subtitle of host publication	First Iberian Conference, IbPRIA 2003 Puerto de Andratx, Mallorca, Spain, June 4–6, 2003 Proceedings
Editors	Francisco Jose Perales, Aurelio J. C. Campilho, Nicolas Perez Perez, Nicolas Perez Perez
Publisher	Springer Nature
Pages	1099-1107
Number of pages	9
ISBN (Electronic)	978-3-540-44871-6
ISBN (Print)	3540402179, 9783540402176
DOIs	https://doi.org/10.1007/978-3-540-44871-6_127
Publication status	Published - 22 May 2003
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2652
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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

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Zwiggelaar, R., Blot, L., Raba, D., & Denton, E. R. E. (2003). Set-Permutation-Occurrence Matrix Based Texture Segmentation. In F. J. Perales, A. J. C. Campilho, N. P. Perez, & N. P. Perez (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): First Iberian Conference, IbPRIA 2003 Puerto de Andratx, Mallorca, Spain, June 4–6, 2003 Proceedings (pp. 1099-1107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2652). Springer Nature. https://doi.org/10.1007/978-3-540-44871-6_127

Zwiggelaar, Reyer ; Blot, Lilian ; Raba, David et al. / Set-Permutation-Occurrence Matrix Based Texture Segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): First Iberian Conference, IbPRIA 2003 Puerto de Andratx, Mallorca, Spain, June 4–6, 2003 Proceedings. editor / Francisco Jose Perales ; Aurelio J. C. Campilho ; Nicolas Perez Perez ; Nicolas Perez Perez. Springer Nature, 2003. pp. 1099-1107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

author = "Reyer Zwiggelaar and Lilian Blot and David Raba and Denton, {Erika R. E.}",

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Zwiggelaar, R, Blot, L, Raba, D & Denton, ERE 2003, Set-Permutation-Occurrence Matrix Based Texture Segmentation. in FJ Perales, AJC Campilho, NP Perez & NP Perez (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): First Iberian Conference, IbPRIA 2003 Puerto de Andratx, Mallorca, Spain, June 4–6, 2003 Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2652, Springer Nature, pp. 1099-1107. https://doi.org/10.1007/978-3-540-44871-6_127

Set-Permutation-Occurrence Matrix Based Texture Segmentation. / Zwiggelaar, Reyer; Blot, Lilian; Raba, David et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): First Iberian Conference, IbPRIA 2003 Puerto de Andratx, Mallorca, Spain, June 4–6, 2003 Proceedings. ed. / Francisco Jose Perales; Aurelio J. C. Campilho; Nicolas Perez Perez; Nicolas Perez Perez. Springer Nature, 2003. p. 1099-1107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2652).

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

TY - CHAP

T1 - Set-Permutation-Occurrence Matrix Based Texture Segmentation

AU - Zwiggelaar, Reyer

AU - Blot, Lilian

AU - Raba, David

AU - Denton, Erika R. E.

PY - 2003/5/22

Y1 - 2003/5/22

N2 - 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.

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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Zwiggelaar R, Blot L, Raba D, Denton ERE. Set-Permutation-Occurrence Matrix Based Texture Segmentation. In Perales FJ, Campilho AJC, Perez NP, Perez NP, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): First Iberian Conference, IbPRIA 2003 Puerto de Andratx, Mallorca, Spain, June 4–6, 2003 Proceedings. Springer Nature. 2003. p. 1099-1107. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-44871-6_127

Set-Permutation-Occurrence Matrix Based Texture Segmentation

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