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Segmentation fusion based on neighboring information for MR brain images

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Abstract

In this paper, we study on how to boost image segmentation algorithms. First of all, a novel fusion scheme is proposed to combine different segmentations with mutual information to reduce misclassified pixels and obtain an accurate segmentation. As the class label of each pixel depends on the pixel’s gray level and neighbors’ labels, the fusion scheme takes both spatial and intensity information of pixels into account. Then, a detail thresholding segmentation case is designed using the proposed fusion scheme. In the case, the local Laplacian filter is used to get the smoothed version of original image. To accelerate segmentation, a discrete curve evolution based Otsu method is employed to segment the original image and its smoothed version to get two different segmentation maps. The fusion scheme is used to fuse the two maps to get the final segmentation result. Experiments on medical MR-T2 brain images are conducted to demonstrate the effectiveness of the proposed segmentation fusion method. The experimental results indicate that the proposed algorithm can improve segmentation accuracy and it is superior to other multilevel thresholding methods.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China for Youths (No.61305046), Jilin Province Science Foundation for Youths (No.20130522117JH), and the Natural Science Foundation of Jilin Province (No.20140101193JC).

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Authors and Affiliations

  1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Yuncong Feng, Xuanjing Shen, Haipeng Chen & Xiaoli Zhang

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Yuncong Feng, Xuanjing Shen, Haipeng Chen & Xiaoli Zhang

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  1. Yuncong Feng
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  2. Xuanjing Shen
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  3. Haipeng Chen
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  4. Xiaoli Zhang
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Correspondence to Haipeng Chen.

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Feng, Y., Shen, X., Chen, H. et al. Segmentation fusion based on neighboring information for MR brain images. Multimed Tools Appl 76, 23139–23161 (2017). https://doi.org/10.1007/s11042-016-4098-3

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  • DOI: https://doi.org/10.1007/s11042-016-4098-3

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