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

^*Corresponding author for this work

Institute of Medical Informatics

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.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2018
Editors	Maier Andreas, Thomas M. Deserno, Heinz Handels, Klaus Hermann Maier-Hein, Christoph Palm, Thomas Tolxdorff
Number of pages	2
Volume	1
Publisher	Springer Verlag
Publication date	01.01.2018
Edition	211279
Pages	37-38
ISBN (Print)	978-3-662-56536-0
ISBN (Electronic)	978-3-662-56537-7
DOIs	https://doi.org/10.1007/978-3-662-56537-7_23
Publication status	Published - 01.01.2018
Event	Bildverarbeitung für die Medizin 2018 - Lehrstuhl für Mustererkennung, Erlangen, Germany Duration: 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

Cite this

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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 (eds), Bildverarbeitung für die Medizin 2018 . 211279 edn, vol. 1, Informatik aktuell, Springer Verlag, pp. 37-38, Bildverarbeitung für die Medizin 2018, Erlangen, Germany, 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 . ed. / Maier Andreas; Thomas M. Deserno; Heinz Handels; Klaus Hermann Maier-Hein; Christoph Palm; Thomas Tolxdorff. Vol. 1 211279. ed. Springer Verlag, 2018. p. 37-38 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AU - Krüger, Julia

AU - Ehrhardt, Jan

AU - Handels, Heinz

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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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BT - Bildverarbeitung für die Medizin 2018

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A2 - Handels, Heinz

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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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