Graph cut-based segmentation

Department of Computer Science

Graph cut-based segmentation

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

Graph cut-based segmentation. / Petersen, Jens; Oguz, Ipek; de Bruijne, Marleen.

Medical Image Analysis. Academic Press, 2023. p. 247-273.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Petersen, J, Oguz, I & de Bruijne, M 2023, Graph cut-based segmentation. in Medical Image Analysis. Academic Press, pp. 247-273. https://doi.org/10.1016/B978-0-12-813657-7.00024-8

APA

Petersen, J., Oguz, I., & de Bruijne, M. (2023). Graph cut-based segmentation. In Medical Image Analysis (pp. 247-273). Academic Press. https://doi.org/10.1016/B978-0-12-813657-7.00024-8

Vancouver

Petersen J, Oguz I, de Bruijne M. Graph cut-based segmentation. In Medical Image Analysis. Academic Press. 2023. p. 247-273 https://doi.org/10.1016/B978-0-12-813657-7.00024-8

Author

Petersen, Jens ; Oguz, Ipek ; de Bruijne, Marleen. / Graph cut-based segmentation. Medical Image Analysis. Academic Press, 2023. pp. 247-273

Bibtex

@inbook{40e11faf84df4e6781d8ffb278ffbe59,

title = "Graph cut-based segmentation",

abstract = "This chapter describes how to use graph cut methods for medical image segmentation. Graph cut methods are designed to solve problems that can be modeled using Markov random fields. A brief introduction to graph theory, flow networks, and Markov Random Fields are therefore given. The chapter shows how a range of segmentation tasks can be formulated as such energy minimization problems and demonstrates how they can be solved with graph cuts. Specific examples of how to segment coronary arteries in computed tomography angiography images and the multilayered surfaces of airways in computed tomography images are given.",

keywords = "Graph cut, Segmentation, Vessels",

author = "Jens Petersen and Ipek Oguz and {de Bruijne}, Marleen",

year = "2023",

doi = "10.1016/B978-0-12-813657-7.00024-8",

language = "English",

isbn = "9780128136584",

pages = "247--273",

booktitle = "Medical Image Analysis",

publisher = "Academic Press",

address = "United States",

}

RIS

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T1 - Graph cut-based segmentation

AU - Petersen, Jens

AU - Oguz, Ipek

AU - de Bruijne, Marleen

PY - 2023

Y1 - 2023

N2 - This chapter describes how to use graph cut methods for medical image segmentation. Graph cut methods are designed to solve problems that can be modeled using Markov random fields. A brief introduction to graph theory, flow networks, and Markov Random Fields are therefore given. The chapter shows how a range of segmentation tasks can be formulated as such energy minimization problems and demonstrates how they can be solved with graph cuts. Specific examples of how to segment coronary arteries in computed tomography angiography images and the multilayered surfaces of airways in computed tomography images are given.

AB - This chapter describes how to use graph cut methods for medical image segmentation. Graph cut methods are designed to solve problems that can be modeled using Markov random fields. A brief introduction to graph theory, flow networks, and Markov Random Fields are therefore given. The chapter shows how a range of segmentation tasks can be formulated as such energy minimization problems and demonstrates how they can be solved with graph cuts. Specific examples of how to segment coronary arteries in computed tomography angiography images and the multilayered surfaces of airways in computed tomography images are given.

KW - Graph cut

KW - Segmentation

KW - Vessels

UR - http://www.scopus.com/inward/record.url?scp=85175383787&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-813657-7.00024-8

DO - 10.1016/B978-0-12-813657-7.00024-8

M3 - Book chapter

AN - SCOPUS:85175383787

SN - 9780128136584

SP - 247

EP - 273

BT - Medical Image Analysis

PB - Academic Press

ER -

ID: 372612745