Dynamic Incorporation ofWavelet Filter in Fuzzy C-Means for Efficient and Noise-Insensitive MR Image Segmentation

DOI

10.1080/18756891.2015.1063241How to use a DOI?

Keywords

MR images, segmentation, fuzzy c-means, clustering, wavelet, Rician noise

Abstract

Image intensity in magnetic resonance (MR) images in the presence of noise obeys Rician distribution. The signal-dependent Rician noise makes accurate image segmentation a challenging task. Although existing fuzzy c-means (FCM) variants with local filters improve the segmentation performance, they are less effective for reducing the negative effect from Rician noise, and the repeatedly applied filter increases their computational intensiveness. To address this issue, we propose a novel image segmentation method which dynamically incorporates wavelet-based noise detector and filter in the FCM membership function. The modified algorithm is designed to exploit both frequency and spatial information in the images and minimizes clustering errors caused by Rician noise. Furthermore, efficiency of the proposed method can be enhanced by the strategy of applying filter only when noise is detected. The experimental results of segmentation on synthetic and brain MR images, demonstrate the computational efficiency and noiseinsensitivity of the proposed method.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Shang-Ling Jui
AU  - Chao Lin
AU  - Weichen Xu
AU  - Weiyao Lin
AU  - Dongmei Wang
AU  - Kai Xiao
PY  - 2015
DA  - 2015/09/01
TI  - Dynamic Incorporation ofWavelet Filter in Fuzzy C-Means for Efficient and Noise-Insensitive MR Image Segmentation
JO  - International Journal of Computational Intelligence Systems
SP  - 796
EP  - 807
VL  - 8
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1063241
DO  - 10.1080/18756891.2015.1063241
ID  - Jui2015
ER  -