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.
Anisotropic spatial information Asymmetric distribution Multi-phase level set Intensity inhomogeneity Magnetic resonance image segmentation
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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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Conflict of interest
The authors declare that they have no conflict of interest.
Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Proc. Comput. Sci. 102, 317–324 (2016)CrossRefGoogle Scholar
Wang, L., Shi, F., Yap, P.T., et al.: Longitudinally guided level sets for consistent tissue segmentation of neonates. Hum. Brain Mapp. 34(4), 956–972 (2013)CrossRefGoogle Scholar
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)MathSciNetCrossRefGoogle Scholar
Gupta, D., Anand, R.S.: A hybrid edge-based segmentation approach for ultrasound medical images. Biomed. Signal Process. Control 31, 116–126 (2017)CrossRefGoogle Scholar
Suganthi, S.S., Ramakrishnan, S.: Anisotropic diffusion filter based edge enhancement for segmentation of breast thermogram using level sets. Biomed. Signal Process. Control 10(1), 128–136 (2014)CrossRefGoogle Scholar
Wang, X.F., Min, H., Zhang, Y.G.: Multi-scale local region based level set method for image segmentation in the presence of intensity inhomogeneity. Neurocomputing 151, 1086–1098 (2015)CrossRefGoogle Scholar
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
Li, C., Kao, C.Y., Gore, J.C., et al.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)MathSciNetCrossRefGoogle Scholar
Li, C., Huang, R., Ding, Z., et al.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)MathSciNetCrossRefGoogle Scholar
Wang, Li, Chen, Yunjie, Ding, Zhaohua, Xia, Deshen: Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy. J. Neurosci. Methods 188(2), 316–325 (2010)CrossRefGoogle Scholar
Wang, Li, Shi, Feng, Lin, Weili, Gilmore, John H., Shen, Dinggang: Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58, 805–817 (2011)CrossRefGoogle Scholar
Chen, Y., Zhao, B., Zhang, J., et al.: Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model. Magn. Reson. Imag. 32(7), 941–955 (2014)CrossRefGoogle Scholar
Zhang, K., Zhang, L., Lam, K.M., et al.: A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans. Cybern. 46(2), 546–557 (2015)CrossRefGoogle Scholar
Meng, X., Gu, W., Chen, Y., et al.: Brain MR image segmentation based on an improved active contour model. PLoS ONE 12(8), e0183943 (2017)CrossRefGoogle Scholar
Brox, T.: From pixels to regions: partial differential equations in image analysis, Ph.D. dissertation, Dept. Comput. Sci., Saarland University, Saarbrücken, Germany (2005)Google Scholar
Nguyen, T.M., Jonathan Wu, Q.M., Mukherjee, D., Zhang, H.: A Bayesian bounded asymmetric mixture model with segmentation application. IEEE J. Biomed. Health Inform. 18, 109–119 (2014)CrossRefGoogle Scholar
Xu, Y., Géraud, T., Bloch, I.: From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning. In: IEEE International Conference on Image Processing (ICIP), pp. 4417–4421 (2017)Google Scholar
You, X., Peng, Q., Yuan, Y., et al.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn. 44(10–11), 2314–2324 (2011)CrossRefGoogle Scholar
Khowaja, S.A., Khuwaja, P., Ismaili, I.A.: A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification. SIViP 13, 379–387 (2019)CrossRefGoogle Scholar