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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 747549, 13 pages
Research Article

A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

1School of Computer Science and Technology, Shandong University, Jinan 250101, China
2College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan 250355, China

Received 22 November 2013; Accepted 9 January 2014; Published 9 March 2014

Academic Editor: Yuanjie Zheng

Copyright © 2014 Shiyong Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image.