Robust extraction of urinary stones from CT data using attribute filters

Georgios K. Ouzounis; Stilianos Giannakopoulos; Constantinos E. Simopoulos; Michael H.F. Wilkinson

Robust extraction of urinary stones from CT data using attribute filters

Georgios K. Ouzounis, Stilianos Giannakopoulos, Constantinos E. Simopoulos, Michael H.F. Wilkinson

Intelligent Systems

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

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Abstract

In medical imaging, anatomical and other structures such as urinary stones, are often extracted with the aid of active contour/surface models. Active surface-based methods have robustness limitations and are computationally expensive. In this paper we present a morphological method based on attribute filters and the newly presented sphericity attribute. The operators involved, extract the targeted objects in their entirety without shape/size distortions and proceed rapidly. Experiments on three real 3D data-sets demonstrate their efficiency and their performance is discussed.

Original language	English
Title of host publication	Proc. 16th International Conference on Image Processing 2009
Publisher	IEEE
Pages	2629-2632
Number of pages	4
ISBN (Print)	978-1-4244-5654-3
Publication status	Published - 2009

Keywords

sphericity
attribute filters
urinary stones

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Robust extraction of urinary stones from CT data using attribute filters
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@inproceedings{a869b6e5e96f497dba6d0567cf46040e,

title = "Robust extraction of urinary stones from CT data using attribute filters",

abstract = "In medical imaging, anatomical and other structures such as urinary stones, are often extracted with the aid of active contour/surface models. Active surface-based methods have robustness limitations and are computationally expensive. In this paper we present a morphological method based on attribute filters and the newly presented sphericity attribute. The operators involved, extract the targeted objects in their entirety without shape/size distortions and proceed rapidly. Experiments on three real 3D data-sets demonstrate their efficiency and their performance is discussed.",

keywords = "sphericity, attribute filters, urinary stones",

author = "Ouzounis, {Georgios K.} and Stilianos Giannakopoulos and Simopoulos, {Constantinos E.} and Wilkinson, {Michael H.F.}",

note = "Relation: http://www.rug.nl/informatica/onderzoek/bernoulli Rights: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science",

year = "2009",

language = "English",

isbn = "978-1-4244-5654-3",

pages = "2629--2632",

booktitle = "Proc. 16th International Conference on Image Processing 2009",

publisher = "IEEE",

}

TY - GEN

T1 - Robust extraction of urinary stones from CT data using attribute filters

AU - Ouzounis, Georgios K.

AU - Giannakopoulos, Stilianos

AU - Simopoulos, Constantinos E.

AU - Wilkinson, Michael H.F.

N1 - Relation: http://www.rug.nl/informatica/onderzoek/bernoulli Rights: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science

PY - 2009

Y1 - 2009

N2 - In medical imaging, anatomical and other structures such as urinary stones, are often extracted with the aid of active contour/surface models. Active surface-based methods have robustness limitations and are computationally expensive. In this paper we present a morphological method based on attribute filters and the newly presented sphericity attribute. The operators involved, extract the targeted objects in their entirety without shape/size distortions and proceed rapidly. Experiments on three real 3D data-sets demonstrate their efficiency and their performance is discussed.

AB - In medical imaging, anatomical and other structures such as urinary stones, are often extracted with the aid of active contour/surface models. Active surface-based methods have robustness limitations and are computationally expensive. In this paper we present a morphological method based on attribute filters and the newly presented sphericity attribute. The operators involved, extract the targeted objects in their entirety without shape/size distortions and proceed rapidly. Experiments on three real 3D data-sets demonstrate their efficiency and their performance is discussed.

KW - sphericity

KW - attribute filters

KW - urinary stones

M3 - Conference contribution

SN - 978-1-4244-5654-3

SP - 2629

EP - 2632

BT - Proc. 16th International Conference on Image Processing 2009

PB - IEEE

ER -

Robust extraction of urinary stones from CT data using attribute filters

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