Abstract
A fully automatic and accurate brain image segmentation divided into the three tissue types—Grey Matter, White Matter and Cerebrospinal Fluid (CSF)—is necessary in medical analysis. Gaussian Mixture Model (GMM) is one of the most widely implemented methods for MR Brain Image Segmentation, and Expectation–Maximization algorithm is used to estimate the parameters of the model. The major drawback existing in conventional GMM is that it considers each pixel as independent and does not incorporate the spatial interaction among the neighbouring pixels. This will lead to poor segmentation accuracy in the presence of noise and intensity inhomogeneity. To overcome this drawback, in the proposed method, the posterior probability is weighted with a spatial factor which improves the segmentation accuracy and makes the convergence also faster. Also, in the automatic segmentation of the IBSR MR Brain Image Data, a new approach of re-labelling the voxels for Grey Matter and CSF is proposed. The proposed method is tested on 20 low-resolution T1-weighted IBSR Brain Images and BrainWeb Simulated MR Images. The results show that the proposed method can improve the performance of MR Brain Image Segmentation by around 4 % over the state-of-the-art algorithm.
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References
Liew, A.; Yan, H.: Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images. Curr. Med. Imaging Rev. 2(1), 91–103 (2006)
Ji, Z.; Xia, Y.; Sun, Q.; Chen, Q.: Fuzzy local Gaussian mixture model for brain MR image segmentation. IEEE Trans. Inf. Technol. Biomed. 16(3), 339–347 (2012)
Ji, Z.; Xia, Y.; Sun, Q.; Chen, Q.; Feng, D.: Adaptive scale fuzzy local Gaussian mixture model for brain MR image segmentation. Neurocomputing 134, 60–69 (2014)
Ji, Z.; Liu, J.; Cao, G.; Sun, Q.; Chen, Q.: Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation. Pattern Recognit. 47, 2454–2466 (2014)
Van Leemput, K.; Maes, F.; Vandermeulen, D.; Suetens, P.: A unifying framework for partial volume segmentation of brain MR images. IEEE Trans. Med. Imaging 22(1), 105–119 (2003)
Greenspan, H.; Ruf, A.; Goldberger, J.: Constrained Gaussian mixture model framework for auotmatic segmentation of MR brain images. IEEE Trans. Med. Imaging 25(9), 1233–1245 (2006)
Mayer, A.; Greenspan, H.: An adaptive mean-shift framework for MRI brain segmentation. IEEE Trans. Med. Imaging 28(8), 1238–1250 (2009)
Lee, J.-D.; Su, H.-R.; Cheng, P.; Liou, M.; Aston, J.; Tsai, A.; Chen, C.Y.: MR image segmentation using a power transformation approach. IEEE Trans. Med. Imaging 28(6), 894–905 (2009)
Scherrer, B.; Forbes, F.; Garbay, C.; Dojat, M.: Distributed local MRF models for tissue and structure brain segmentation. IEEE Trans. Med. Imag. 28(8), 1278–1295 (2009)
Richard, N.; Dojat, M.; Garbay, C.: Distributed Markovian segmentation: application to MR brain scans. Pattern Recognit. 40(12), 3467–3480 (2007)
Demirhan, A.; Güler, İ.: Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng. Appl. Artif. Intell. 24(2), 358–367 (2011)
Tian, G.J.; Xia, Y.; Zhang, Y.; Feng, D.: Hybrid genetic and variational expectation-maximization algorithm for Gaussian-mixture-model-based brain MR image segmentation. IEEE Trans. Inf Technol. Biomed. 15(13), 373–880 (2011)
Portela, N.; Cavalcanti, G.; Ren, T.: Semi-supervised clustering for MR brain image Segmentation. Expert Syst. Appl. 41(4), 1492–1497 (2014)
Nguyen, T.M.; Wu, Q.: Gaussian-mixture-model-based spatial neighborhood relationships for pixel labeling problem. IEEE Trans. Syst. Man Cybern. 42(1), 193–202 (2012)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Dempster, A.; Laird, N.; Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)
Van Leemput, K.; Maes, F.; Vandermeulen, D.; Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 885–896 (1999)
Manjon, J.; Carbonell-Caballero, J.; Garcia-Marti, G.; Marti-Bonmati, L.; Robles, M.: MRI denoising using non-local means. Med. Image Anal. 12, 514–523 (2008)
Buades, A.; Coll, B.; Morel, J.: A non-local algorithm for image denoising. In: IEEE International Conference on Computer Vision and Pattern Recognition (2005)
Zhao, F.; Fan, J.; Liu, H.: Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation. Expert Syst. Appl. 41, 4083–4093 (2014)
Carson, C.; Belongie, S.; Greenspan, H.; Malik, J.: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002)
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Meena Prakash, R., Kumari, R.S.S. Gaussian Mixture Model with the Inclusion of Spatial Factor and Pixel Re-labelling: Application to MR Brain Image Segmentation. Arab J Sci Eng 42, 595–605 (2017). https://doi.org/10.1007/s13369-016-2278-0
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DOI: https://doi.org/10.1007/s13369-016-2278-0