NDLI: A brain tissue segmentation approach integrating fuzzy information into level set method

Content Provider	IEEE Xplore Digital Library
Author	Zhibin Chen Tianshuang Qiu Su Ruan
Copyright Year	2008
Description	Author affiliation: Dept. of Electron. Eng., Dalian Univ. of Technol., Dalian (Zhibin Chen)
Abstract	In this paper, we propose a new segmentation approach based on level set techniques to segment the brain MR images. We adopt a new binary regional term based on the fuzzy information of the image in the new algorithm, which can inflate or contract the evolving curves automatically without predefined the evolving directions during the initialization phase. The algorithm can segment brain tissues from the different modalities MR images with the same parameters. We compare the performance of the new algorithm with the primary algorithm by simulated experiments. We also explore the influence of the parameter setting and the binary processing of regional term to the algorithm by experiments and statistical analysis. The quantitative and qualitative analysis show that the new algorithm provides more accurate segmentation results with good robustness, and is less sensitive to parameter setting. Furthermore, the binary processing of the regional term greatly decreases the number of iterations; namely, it makes convergence of the new algorithm more quickly.
Starting Page	1216
Ending Page	1221
File Size	237611
Page Count	6
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Image segmentation brain magnetic resonance images Magnetic resonance imaging Clustering algorithms Brain modeling Brain models segmentation Robustness fuzzy c-means Level Se method
Content Type	Text
Resource Type	Article

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