Texture Segmentation in Mammograms

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

Texture Segmentation in Mammograms

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

University of East Anglia
Universitat de Girona
Norfolk and Norwich University Hospitals NHS Foundation Trust

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

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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 dimensionality reduction, data generalisation and noise removal purposes. Expectation maximisation modelling of the resulting feature vector provides the basis for image segmentation. The developed segmentation results are used for automatic mammographic risk assessment

Iaith wreiddiol	Saesneg
Tudalennau	137-140
Nifer y tudalennau	4
Statws	Cyhoeddwyd - 10 Gorff 2003
Cyhoeddwyd yn allanol	Ie
Digwyddiad	Medical Image Understanding and Analysis 2003 - University of Sheffield, Sheffield, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon Hyd: 10 Gorff 2003 → 11 Gorff 2003

Cynhadledd

Cynhadledd	Medical Image Understanding and Analysis 2003
Teitl cryno	MIUA 2003
Gwlad/Tiriogaeth	Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Dinas	Sheffield
Cyfnod	10 Gorff 2003 → 11 Gorff 2003

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Dolen barhaus

Dyfynnu hyn

@conference{12e8b64c1c0a498383b7ceace9268290,

title = "Texture Segmentation in Mammograms",

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 dimensionality reduction, data generalisation and noise removal purposes. Expectation maximisation modelling of the resulting feature vector provides the basis for image segmentation. 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.\}",

year = "2003",

month = jul,

day = "10",

language = "English",

pages = "137--140",

note = "Medical Image Understanding and Analysis 2003, MIUA 2003 ; Conference date: 10-07-2003 Through 11-07-2003",

}

TY - CONF

T1 - Texture Segmentation in Mammograms

AU - Zwiggelaar, Reyer

AU - Blot, Lilian

AU - Raba, David

AU - Denton, Erika R. E.

PY - 2003/7/10

Y1 - 2003/7/10

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 dimensionality reduction, data generalisation and noise removal purposes. Expectation maximisation modelling of the resulting feature vector provides the basis for image segmentation. 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 dimensionality reduction, data generalisation and noise removal purposes. Expectation maximisation modelling of the resulting feature vector provides the basis for image segmentation. The developed segmentation results are used for automatic mammographic risk assessment

M3 - Paper

SP - 137

EP - 140

T2 - Medical Image Understanding and Analysis 2003

Y2 - 10 July 2003 through 11 July 2003

ER -

Texture Segmentation in Mammograms

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