EVALUATION AND IMPROVEMENTS OF THE LEVEL SET METHOD FOR RM IMAGES SEGMENTATION

Donatello Conte, Pasquale Foggia, Francesco Tufano, Mario Vento

Abstract

We present a novel algorithm for the segmentation of bony tissues in MR images. Our approach is based on the level set algorithm. We introduce some pre-processing phases that improve image quality and segmentation performance. The technique requires no training and operates semi-automatically, requiring only the entry of a single seed point within the tissue to be segmented. The proposed approach is more robust than the other approaches present in the literature, with respect to the position of the initial seed point. The quantitative analysis of the results on a significant number of images demonstrate the effectiveness of our approach.

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Paper Citation


in Harvard Style

Conte D., Foggia P., Tufano F. and Vento M. (2009). EVALUATION AND IMPROVEMENTS OF THE LEVEL SET METHOD FOR RM IMAGES SEGMENTATION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 210-215. DOI: 10.5220/0001804102100215


in Bibtex Style

@conference{visapp09,
author={Donatello Conte and Pasquale Foggia and Francesco Tufano and Mario Vento},
title={EVALUATION AND IMPROVEMENTS OF THE LEVEL SET METHOD FOR RM IMAGES SEGMENTATION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={210-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001804102100215},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - EVALUATION AND IMPROVEMENTS OF THE LEVEL SET METHOD FOR RM IMAGES SEGMENTATION
SN - 978-989-8111-69-2
AU - Conte D.
AU - Foggia P.
AU - Tufano F.
AU - Vento M.
PY - 2009
SP - 210
EP - 215
DO - 10.5220/0001804102100215