3D fuzzy liver tumor segmentation

Paweł Badura; Ewa Pietka

doi:10.1007/978-3-642-31196-3_5

3D fuzzy liver tumor segmentation

Department of Medical Informatics and Artificial Intelligence (RIB/RIB1)

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

10 Citations (Scopus)

Abstract

A semi-automated method for segmentation of liver nodules in Computed Tomography studies is described in this paper. The application is part of a liver cancer computer-aided diagnosis (CAD) system. Its main body consists of the three-dimensional anisotropic diffusion filtering and the adaptive region growing, supported by the fuzzy inference system. Such a workflow enables elimination of noise within the image data, enhances nodule region boundaries, and cuts "segmentation leaks". The outcome is interactively presented to the physician with a possibility left to make manual adjustments. The system has been evaluated using a database of 17 abdominal Computed Tomography studies including 30 various liver nodules outlined by the radiologist, yielding 77% effectiveness (23 cases).

Original language	English
Title of host publication	Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings
Pages	47-57
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-31196-3_5
Publication status	Published - 2012
Event	3rd International Conference on Information Technologies in Biomedicine, ITIB 2012 - Gliwice, Poland Duration: 11 Jun 2012 → 13 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7339 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Information Technologies in Biomedicine, ITIB 2012
Country/Territory	Poland
City	Gliwice
Period	11/06/12 → 13/06/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

anisotropic diffusion
fuzzy inference system
liver tumor
segmentation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-31196-3_5

Cite this

Badura, P., & Pietka, E. (2012). 3D fuzzy liver tumor segmentation. In Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings (pp. 47-57). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7339 LNBI). https://doi.org/10.1007/978-3-642-31196-3_5

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title = "3D fuzzy liver tumor segmentation",

abstract = "A semi-automated method for segmentation of liver nodules in Computed Tomography studies is described in this paper. The application is part of a liver cancer computer-aided diagnosis (CAD) system. Its main body consists of the three-dimensional anisotropic diffusion filtering and the adaptive region growing, supported by the fuzzy inference system. Such a workflow enables elimination of noise within the image data, enhances nodule region boundaries, and cuts {"}segmentation leaks{"}. The outcome is interactively presented to the physician with a possibility left to make manual adjustments. The system has been evaluated using a database of 17 abdominal Computed Tomography studies including 30 various liver nodules outlined by the radiologist, yielding 77\% effectiveness (23 cases).",

keywords = "anisotropic diffusion, fuzzy inference system, liver tumor, segmentation",

author = "Pawe{\l} Badura and Ewa Pietka",

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language = "English",

isbn = "9783642311956",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "3rd International Conference on Information Technologies in Biomedicine, ITIB 2012 ; Conference date: 11-06-2012 Through 13-06-2012",

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Badura, P & Pietka, E 2012, 3D fuzzy liver tumor segmentation. in Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7339 LNBI, pp. 47-57, 3rd International Conference on Information Technologies in Biomedicine, ITIB 2012, Gliwice, Poland, 11/06/12. https://doi.org/10.1007/978-3-642-31196-3_5

3D fuzzy liver tumor segmentation. / Badura, Paweł ; Pietka, Ewa.
Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings. 2012. p. 47-57 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7339 LNBI).

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

TY - GEN

T1 - 3D fuzzy liver tumor segmentation

AU - Badura, Paweł

AU - Pietka, Ewa

PY - 2012

Y1 - 2012

N2 - A semi-automated method for segmentation of liver nodules in Computed Tomography studies is described in this paper. The application is part of a liver cancer computer-aided diagnosis (CAD) system. Its main body consists of the three-dimensional anisotropic diffusion filtering and the adaptive region growing, supported by the fuzzy inference system. Such a workflow enables elimination of noise within the image data, enhances nodule region boundaries, and cuts "segmentation leaks". The outcome is interactively presented to the physician with a possibility left to make manual adjustments. The system has been evaluated using a database of 17 abdominal Computed Tomography studies including 30 various liver nodules outlined by the radiologist, yielding 77% effectiveness (23 cases).

AB - A semi-automated method for segmentation of liver nodules in Computed Tomography studies is described in this paper. The application is part of a liver cancer computer-aided diagnosis (CAD) system. Its main body consists of the three-dimensional anisotropic diffusion filtering and the adaptive region growing, supported by the fuzzy inference system. Such a workflow enables elimination of noise within the image data, enhances nodule region boundaries, and cuts "segmentation leaks". The outcome is interactively presented to the physician with a possibility left to make manual adjustments. The system has been evaluated using a database of 17 abdominal Computed Tomography studies including 30 various liver nodules outlined by the radiologist, yielding 77% effectiveness (23 cases).

KW - anisotropic diffusion

KW - fuzzy inference system

KW - liver tumor

KW - segmentation

UR - https://www.scopus.com/pages/publications/84862507631

U2 - 10.1007/978-3-642-31196-3_5

DO - 10.1007/978-3-642-31196-3_5

M3 - Conference contribution

AN - SCOPUS:84862507631

SN - 9783642311956

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 47

EP - 57

BT - Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings

T2 - 3rd International Conference on Information Technologies in Biomedicine, ITIB 2012

Y2 - 11 June 2012 through 13 June 2012

ER -

3D fuzzy liver tumor segmentation

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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