AUTOMATED TUMOR SEGMENTATION USING LEVEL SET METHODS

Stephane Lebonvallet, Sonia Khatchadourian, Su Ruan

Abstract

In the framework of detection, diagnostic and treatment planning of the tumours, the Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) have become the most efficient techniques for body and brain examination. Radiologists take usually several hours to segment manually the region of interest (ROI) on images to obtain some information about patient pathology. It is very time consuming. The aim of our study is to propose an automatic solution to this problem to help the radiologist’s work. This paper presents an approach of tumour segmentation based on a fast level set method. The results obtained by the proposed method dealing with both PET and MRI images are encouraging.

References

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Paper Citation


in Harvard Style

Lebonvallet S., Khatchadourian S. and Ruan S. (2007). AUTOMATED TUMOR SEGMENTATION USING LEVEL SET METHODS . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Computer Vision Methods in Medicine, (VISAPP 2007) ISBN 978-972-8865-75-7, pages 128-133. DOI: 10.5220/0002068301280133


in Bibtex Style

@conference{computer vision methods in medicine07,
author={Stephane Lebonvallet and Sonia Khatchadourian and Su Ruan},
title={AUTOMATED TUMOR SEGMENTATION USING LEVEL SET METHODS},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Computer Vision Methods in Medicine, (VISAPP 2007)},
year={2007},
pages={128-133},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002068301280133},
isbn={978-972-8865-75-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Computer Vision Methods in Medicine, (VISAPP 2007)
TI - AUTOMATED TUMOR SEGMENTATION USING LEVEL SET METHODS
SN - 978-972-8865-75-7
AU - Lebonvallet S.
AU - Khatchadourian S.
AU - Ruan S.
PY - 2007
SP - 128
EP - 133
DO - 10.5220/0002068301280133