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

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

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

12 Citations (Scopus)

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.

Original language	English
Title of host publication	Information Technologies in Biomedicine
Subtitle of host publication	Volume 2
Editors	Ewa Pietka, Jacek Kawa
Pages	91-100
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-13105-9_10
Publication status	Published - 2010

Publication series

Name	Advances in Intelligent and Soft Computing
Volume	69
ISSN (Print)	1867-5662

UN SDGs

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

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

General Computer Science

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

Cite this

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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.",

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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

Abstract

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