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Licensed Unlicensed Requires Authentication Published by De Gruyter March 4, 2014

Level set method coupled with Energy Image features for brain MR image segmentation

Mirela (Visan) Punga , Rahul Gaurav and Luminita Moraru EMAIL logo

Abstract

Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels’ values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.


Corresponding author: Luminita Moraru, Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 47 Domneasca St., 800008 Galati, Romania, Phone: +40745649014, Fax: +40236461353, E-mail:

Acknowledgments

The authors would like to thank Dr. Adina-Geanina Nămoianu, St. Andrew Emergency Hospital, Galati, Romania, for useful discussions.

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Received: 2013-10-17
Accepted: 2014-2-10
Published Online: 2014-3-4
Published in Print: 2014-6-1

©2014 by Walter de Gruyter Berlin/Boston

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