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Proceedings Paper

Brain tumor segmentation in MRI based on fuzzy aggregators
Author(s): Yan Zhu; Qingmin Liao; Weibei Dou; Su Ruan
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Paper Abstract

Magnetic Resonance Image (MRI) is widely used in radiology diagnosis, especially in pathology detection in human brain. Most of the methods now applied to automatically segment brain tumors rely on T1-weighted sequences exclusively despite the fact that the imaging agent is multi-spectral. The work focuses on the integration or fusion of information provided by each sequence, i.e. T1, T2 and PD. Based on the fuzzy aggregators proposed in fuzzy theory, a system integrating all these information is established. The paper discusses some famous operators, their properties and application in tumor segmentation. In particular, Davies-Bouldin index is used to determine the parameters of the parametric operations. The result shows the importance of data fusion in segmentation process, discovers that T-norms are less robust to noise compared with mean operators. Meanwhile, weights allocated illustrate the order of importance of each spectrum in pathology detection, and are in agreement with their characteristic.

Paper Details

Date Published: 24 June 2005
PDF: 8 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 596050 (24 June 2005); doi: 10.1117/12.633220
Show Author Affiliations
Yan Zhu, Tsinghua Univ. (China)
Qingmin Liao, Tsinghua Univ. (China)
Weibei Dou, GREYC, CNRS (France)
Tsinghua Univ. (China)
Su Ruan, GREYC, CNRS (France)


Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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