4D segmentation of ewing's sarcoma in MR images

Joanna Czajkowska; Pawel Badura; Ewa Pietka

doi:10.1007/978-3-642-13105-9_10

4D segmentation of ewing's sarcoma in MR images

Katedra Informatyki Medycznej i Sztucznej Inteligencji (RIB/RIB1)

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

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Abstrakt

This paper presents the first stage of a semi-automatic method for the segmentation of nodules of the skeleto-muscular system from magnetic resonance (MR) imaging. Themethod suggested is efficient irrespective of the tumour location in human body. It is based on Fuzzy C-Means clustering (FCM), Gaussian Mixture Models (GMM) and Fuzzy Connectedness analysis applied to the dataset consisting of T1W and T2W series. In this study a method of transforming the results between planes is also presented. The suggested algorithm has been evaluated on the examinations of different parts of the body, where Ewing's sarcomas have been indicated by a radiologist.

Język oryginału	angielski
Tytuł publikacji goszczącej	Information Technologies in Biomedicine
Podtytuł publikacji goszczącej	Volume 2
Redaktorzy	Ewa Pietka, Jacek Kawa
Strony	91-100
Liczba stron	10
Identyfikatory DOI	https://doi.org/10.1007/978-3-642-13105-9_10
Status publikacji	Opublikowano - 2010

Seria publikacji

Nazwa	Advances in Intelligent and Soft Computing
Tom	69
ISSN (drukowany)	1867-5662

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 ogólna

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10.1007/978-3-642-13105-9_10

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title = "4D segmentation of ewing's sarcoma in MR images",

abstract = "This paper presents the first stage of a semi-automatic method for the segmentation of nodules of the skeleto-muscular system from magnetic resonance (MR) imaging. Themethod suggested is efficient irrespective of the tumour location in human body. It is based on Fuzzy C-Means clustering (FCM), Gaussian Mixture Models (GMM) and Fuzzy Connectedness analysis applied to the dataset consisting of T1W and T2W series. In this study a method of transforming the results between planes is also presented. The suggested algorithm has been evaluated on the examinations of different parts of the body, where Ewing's sarcomas have been indicated by a radiologist.",

author = "Joanna Czajkowska and Pawel Badura and Ewa Pietka",

year = "2010",

doi = "10.1007/978-3-642-13105-9\_10",

language = "English",

isbn = "9783642131042",

series = "Advances in Intelligent and Soft Computing",

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T1 - 4D segmentation of ewing's sarcoma in MR images

AU - Czajkowska, Joanna

AU - Badura, Pawel

AU - Pietka, Ewa

PY - 2010

Y1 - 2010

N2 - This paper presents the first stage of a semi-automatic method for the segmentation of nodules of the skeleto-muscular system from magnetic resonance (MR) imaging. Themethod suggested is efficient irrespective of the tumour location in human body. It is based on Fuzzy C-Means clustering (FCM), Gaussian Mixture Models (GMM) and Fuzzy Connectedness analysis applied to the dataset consisting of T1W and T2W series. In this study a method of transforming the results between planes is also presented. The suggested algorithm has been evaluated on the examinations of different parts of the body, where Ewing's sarcomas have been indicated by a radiologist.

AB - This paper presents the first stage of a semi-automatic method for the segmentation of nodules of the skeleto-muscular system from magnetic resonance (MR) imaging. Themethod suggested is efficient irrespective of the tumour location in human body. It is based on Fuzzy C-Means clustering (FCM), Gaussian Mixture Models (GMM) and Fuzzy Connectedness analysis applied to the dataset consisting of T1W and T2W series. In this study a method of transforming the results between planes is also presented. The suggested algorithm has been evaluated on the examinations of different parts of the body, where Ewing's sarcomas have been indicated by a radiologist.

UR - https://www.scopus.com/pages/publications/84856340206

U2 - 10.1007/978-3-642-13105-9_10

DO - 10.1007/978-3-642-13105-9_10

M3 - Chapter

AN - SCOPUS:84856340206

SN - 9783642131042

T3 - Advances in Intelligent and Soft Computing

SP - 91

EP - 100

BT - Information Technologies in Biomedicine

A2 - Pietka, Ewa

A2 - Kawa, Jacek

ER -

4D segmentation of ewing's sarcoma in MR images

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Obszary tematyczne ASJC Scopus

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