EM Texture Segmentation of Mammographic Images

Reyer Zwiggelaar; Pere Planiol; Joan Marti; Robert Marti; Lilian Blot; Erika R. E. Denton; Caroline M. E. Rubin

doi:10.1007/978-3-642-59327-7_53

EM Texture Segmentation of Mammographic Images

Reyer Zwiggelaar, Pere Planiol, Joan Marti, Robert Marti, Lilian Blot, Erika R. E. Denton, Caroline M. E. Rubin

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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. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling are compared to expectation maximisation modelling based on single grey-level values. The developed segmentation results are used for two specific applications which are registration of mammographic images and mammographic risk assessment.

Iaith wreiddiol	Saesneg
Teitl	Digital Mammography
Is-deitl	IWDM 2002 — 6th International Workshop on Digital Mammography
Golygyddion	Hainz-Otto Peitgen
Tudalennau	223-227
Nifer y tudalennau	5
ISBN (Electronig)	978-3-642-59327-7
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-642-59327-7_53
Statws	Cyhoeddwyd - 27 Medi 2011
Cyhoeddwyd yn allanol	Ie
Digwyddiad	6th International Workshop on Digital Mammography - Bremen, Yr Almaen Hyd: 22 Meh 2002 → 25 Meh 2002

Gweithdy

Gweithdy	6th International Workshop on Digital Mammography
Teitl cryno	IWDM 2002
Gwlad/Tiriogaeth	Yr Almaen
Dinas	Bremen
Cyfnod	22 Meh 2002 → 25 Meh 2002

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10.1007/978-3-642-59327-7_53

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@inproceedings{dacacde92b904c8bba18d60ced8b68b5,

title = "EM Texture Segmentation of Mammographic Images",

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. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling are compared to expectation maximisation modelling based on single grey-level values. The developed segmentation results are used for two specific applications which are registration of mammographic images and mammographic risk assessment.",

author = "Reyer Zwiggelaar and Pere Planiol and Joan Marti and Robert Marti and Lilian Blot and Denton, {Erika R. E.} and Rubin, {Caroline M. E.}",

year = "2011",

month = sep,

day = "27",

doi = "10.1007/978-3-642-59327-7_53",

language = "English",

isbn = "978-3-642-63936-4",

pages = "223--227",

editor = "Hainz-Otto Peitgen",

booktitle = "Digital Mammography",

note = "6th International Workshop on Digital Mammography, IWDM 2002 ; Conference date: 22-06-2002 Through 25-06-2002",

}

Zwiggelaar, R, Planiol, P, Marti, J, Marti, R, Blot, L, Denton, ERE & Rubin, CME 2011, EM Texture Segmentation of Mammographic Images. yn H-O Peitgen (gol.), Digital Mammography: IWDM 2002 — 6th International Workshop on Digital Mammography. tt. 223-227, 6th International Workshop on Digital Mammography, Bremen, Yr Almaen, 22 Meh 2002. https://doi.org/10.1007/978-3-642-59327-7_53

TY - GEN

T1 - EM Texture Segmentation of Mammographic Images

AU - Zwiggelaar, Reyer

AU - Planiol, Pere

AU - Marti, Joan

AU - Marti, Robert

AU - Blot, Lilian

AU - Denton, Erika R. E.

AU - Rubin, Caroline M. E.

PY - 2011/9/27

Y1 - 2011/9/27

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. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling are compared to expectation maximisation modelling based on single grey-level values. The developed segmentation results are used for two specific applications which are registration of mammographic images and 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. Statistical modelling is used for data generalisation and noise removal purposes. Expectation maximisation modelling of the spatial information in combination with the statistical modelling are compared to expectation maximisation modelling based on single grey-level values. The developed segmentation results are used for two specific applications which are registration of mammographic images and mammographic risk assessment.

U2 - 10.1007/978-3-642-59327-7_53

DO - 10.1007/978-3-642-59327-7_53

M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-642-63936-4

SP - 223

EP - 227

BT - Digital Mammography

A2 - Peitgen, Hainz-Otto

T2 - 6th International Workshop on Digital Mammography

Y2 - 22 June 2002 through 25 June 2002

ER -

EM Texture Segmentation of Mammographic Images

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