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A level set method for brain MR image segmentation under asymmetric distributions

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Abstract

Magnetic resonance (MR) image segmentation plays an essential role for brain disease diagnosis; however, suffered from low contrast, intensity inhomogeneity, noise and asymmetry of the intensity distribution, segmentation methods are always difficult to achieve satisfactory results. In this paper, we propose a novel level set method for brain MR image segmentation with consideration of these problems. We firstly generate a new region descriptor based on asymmetric Gaussian distributions in order to fit different shapes of observed nonsymmetric data. Secondly, we utilize the spatial distance and intensity similarity information of neighborhood pixels to extract local anisotropic spatial information to balance the noise reduction and detail preservation. After that, the extracted information and bias field information are combined to improve the asymmetric region descriptor utilized in the level set framework. Finally, we define a maximum likelihood energy functional on the whole image, integrating the local anisotropic spatial information, the bias field information and the asymmetric distributions. The experimental results on synthetic and clinical images demonstrated that our method can achieve desirable performance in spite of the severe noise and intensity inhomogeneity.

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China 61672291, 61701222, in part by the Nature Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 17KJB510026.

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Authors and Affiliations

  1. School of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yunjie Chen

  2. School of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816, China

    Menglin Wu

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  1. Yunjie Chen
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  2. Menglin Wu
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Correspondence to Menglin Wu.

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Chen, Y., Wu, M. A level set method for brain MR image segmentation under asymmetric distributions. SIViP 13, 1421–1429 (2019). https://doi.org/10.1007/s11760-019-01491-8

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