Probabilistic appearance models for medical image analysis

Julia Krüger; Jan Ehrhardt; Heinz Handels

doi:10.1007/978-3-662-56537-7_23

Probabilistic appearance models for medical image analysis

Julia Krüger^*, Jan Ehrhardt, Heinz Handels

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Medizinische Informatik

Abstract

The identification of one-to-one point correspondences between image objects is one key aspect and at the same time the most challenging part of generating statistical shape and appearance models. Using probabilistic correspondences between samples instead of accurately placed landmarks for shape models [1] eliminated the need of extensive and time consuming landmark and correspondence determination, and furthermore, the dependency of the quality of the generated model on potentially wrong correspondences was reduced.

Originalsprache	Englisch
Titel	Bildverarbeitung für die Medizin 2018
Redakteure/-innen	Maier Andreas, Thomas M. Deserno, Heinz Handels, Klaus Hermann Maier-Hein, Christoph Palm, Thomas Tolxdorff
Seitenumfang	2
Band	1
Herausgeber (Verlag)	Springer Verlag
Erscheinungsdatum	01.01.2018
Auflage	211279
Seiten	37-38
ISBN (Print)	978-3-662-56536-0
ISBN (elektronisch)	978-3-662-56537-7
DOIs	https://doi.org/10.1007/978-3-662-56537-7_23
Publikationsstatus	Veröffentlicht - 01.01.2018
Veranstaltung	Workshop on Bildverarbeitung fur die Medizin 2018 - Lehrstuhl für Mustererkennung, Erlangen, Deutschland Dauer: 11.03.2018 → 13.03.2018 https://www.springer.com/us/book/9783662565360 http://www.bvm-workshop.org

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10.1007/978-3-662-56537-7_23

https://www.springer.com/de/book/9783662565360

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@inproceedings{7f98499d53104ca69910bdb6b23ff0db,

title = "Probabilistic appearance models for medical image analysis",

abstract = "The identification of one-to-one point correspondences between image objects is one key aspect and at the same time the most challenging part of generating statistical shape and appearance models. Using probabilistic correspondences between samples instead of accurately placed landmarks for shape models [1] eliminated the need of extensive and time consuming landmark and correspondence determination, and furthermore, the dependency of the quality of the generated model on potentially wrong correspondences was reduced.",

author = "Julia Kr{\"u}ger and Jan Ehrhardt and Heinz Handels",

year = "2018",

month = jan,

day = "1",

doi = "10.1007/978-3-662-56537-7_23",

language = "English",

isbn = "978-3-662-56536-0",

volume = "1",

series = "Informatik aktuell",

publisher = "Springer Verlag",

pages = "37--38",

editor = "Maier Andreas and Deserno, {Thomas M.} and Heinz Handels and Maier-Hein, {Klaus Hermann} and Christoph Palm and Thomas Tolxdorff",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2018",

edition = "211279",

note = "Bildverarbeitung f{\"u}r die Medizin 2018 ; Conference date: 11-03-2018 Through 13-03-2018",

url = "https://www.springer.com/us/book/9783662565360, http://www.bvm-workshop.org",

}

Krüger, J, Ehrhardt, J & Handels, H 2018, Probabilistic appearance models for medical image analysis. in M Andreas, TM Deserno, H Handels, KH Maier-Hein, C Palm & T Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2018 . 211279 Aufl., Bd. 1, Informatik aktuell, Springer Verlag, S. 37-38, Workshop on Bildverarbeitung fur die Medizin 2018, Erlangen, Deutschland, 11.03.18. https://doi.org/10.1007/978-3-662-56537-7_23

Probabilistic appearance models for medical image analysis. / Krüger, Julia; Ehrhardt, Jan ; Handels, Heinz.
Bildverarbeitung für die Medizin 2018 . Hrsg. / Maier Andreas; Thomas M. Deserno; Heinz Handels; Klaus Hermann Maier-Hein; Christoph Palm; Thomas Tolxdorff. Band 1 211279. Aufl. Springer Verlag, 2018. S. 37-38 (Informatik aktuell).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Probabilistic appearance models for medical image analysis

AU - Krüger, Julia

AU - Ehrhardt, Jan

AU - Handels, Heinz

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The identification of one-to-one point correspondences between image objects is one key aspect and at the same time the most challenging part of generating statistical shape and appearance models. Using probabilistic correspondences between samples instead of accurately placed landmarks for shape models [1] eliminated the need of extensive and time consuming landmark and correspondence determination, and furthermore, the dependency of the quality of the generated model on potentially wrong correspondences was reduced.

AB - The identification of one-to-one point correspondences between image objects is one key aspect and at the same time the most challenging part of generating statistical shape and appearance models. Using probabilistic correspondences between samples instead of accurately placed landmarks for shape models [1] eliminated the need of extensive and time consuming landmark and correspondence determination, and furthermore, the dependency of the quality of the generated model on potentially wrong correspondences was reduced.

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U2 - 10.1007/978-3-662-56537-7_23

DO - 10.1007/978-3-662-56537-7_23

M3 - Conference contribution

AN - SCOPUS:85043322164

SN - 978-3-662-56536-0

VL - 1

T3 - Informatik aktuell

SP - 37

EP - 38

BT - Bildverarbeitung für die Medizin 2018

A2 - Andreas, Maier

A2 - Deserno, Thomas M.

A2 - Handels, Heinz

A2 - Maier-Hein, Klaus Hermann

A2 - Palm, Christoph

A2 - Tolxdorff, Thomas

PB - Springer Verlag

T2 - Bildverarbeitung für die Medizin 2018

Y2 - 11 March 2018 through 13 March 2018

ER -

Probabilistic appearance models for medical image analysis

Abstract

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