3D fuzzy liver tumor segmentation

Paweł Badura; Ewa Pietka

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

3D fuzzy liver tumor segmentation

Katedra Informatyki Medycznej i Sztucznej Inteligencji (RIB/RIB1)

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

10 Cytowania z bazy Scopus

Abstrakt

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).

Język oryginału	angielski
Tytuł publikacji goszczącej	Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings
Strony	47-57
Liczba stron	11
Identyfikatory DOI	https://doi.org/10.1007/978-3-642-31196-3_5
Status publikacji	Opublikowano - 2012
Wydarzenie	3rd International Conference on Information Technologies in Biomedicine, ITIB 2012 - Gliwice, Polska Czas trwania: 11 cze 2012 → 13 cze 2012

Seria publikacji

Nazwa	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Tom	7339 LNBI
ISSN (drukowany)	0302-9743
ISSN (elektroniczny)	1611-3349

Konferencja

Konferencja	3rd International Conference on Information Technologies in Biomedicine, ITIB 2012
Kraj/Terytorium	Polska
Miejscowość	Gliwice
Okres	11/06/12 → 13/06/12

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 3 Dobre zdrowie i dobre samopoczucie

Obszary tematyczne ASJC Scopus

Informatyka teoretyczna
Informatyka ogólna

Uzyskaj dostęp do dokumentu

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

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

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

@inproceedings{0da8d732f79344ce9d8f0553a7132d6f,

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",

year = "2012",

doi = "10.1007/978-3-642-31196-3\_5",

language = "English",

isbn = "9783642311956",

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

pages = "47--57",

booktitle = "Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings",

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

}

Badura, P & Pietka, E 2012, 3D fuzzy liver tumor segmentation. w 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), tom 7339 LNBI, str. 47-57, 3rd International Conference on Information Technologies in Biomedicine, ITIB 2012, Gliwice, Polska, 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. str. 47-57 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 7339 LNBI).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

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

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

Uzyskaj dostęp do dokumentu

Inne pliki i linki

Fingerprint

Cytowanie