Abstract
An integrated method using the adaptive segmentation of brain tissues in Magnetic Resonance Imaging (MRI) images is proposed in this paper. Firstly, we give a template of brain to remove the extra-cranial tissues. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, result of classical watershed algorithm on gray-scale textured images such as tissue images is over-segmentation. The following procedure is a merging process for the over-segmentation regions using fuzzy clustering algorithm (Fuzzy C-Means). But there are still some regions which are not partitioned completely, particularly in the transitional regions between gray matter and white matter. So we proposed a rule-based re-segmentation processing approach to partition these regions. This integrated scheme yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.
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References
Pham, D.L., Xu, C.Y., Prince, J.L.: A survey of current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2, 315–337 (2000); Technical report version, JHU/ECE 99—01, Johns Hopkins University
Wells, W.M., Grimson, W.E.L., Kikinis, R., Arrdrige, S.R.: Adaptive segmentation of MRI data. IEEE Trans Med Imaging 15, 429–442 (1996)
Lorenz, C., Krahnstoever, N.: 3D statistical shape models for medical image segmentation. In: Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling 3DIM 1999, pp. 394–404 (1999)
Kapur, T., Eric, W., Grimson, L., Wells III, W.M.: Segmentation of brain tissue from magnetic resonance images. Medical Image Analysis 1(2), 109–127 (1996)
Bezdek, J., Hall, L., Clarke, L.: Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20(4), 1033–1048 (1993)
Clark, M., Hall, L., Goldgof, D., Clarke, L., Velthuizen, R., Silbiger, M.: MRI segmentation using fuzzy clustering techniques. IEEE Eng. Med. Biol. Mag. 13(5), 730–742 (1994)
Clarke, L., Velthuizen, R., Camacho, M., Heine, J., Vaidyanathan, M., Hall, L., Thatcher, R., Silbiger, M.: MRI segmentation: methods and application. Magn. Reson. Imaging 13(3), 343–368 (1994)
Ryu, Y.-S., Oh, S.-Y.: Automatic extraction of eye and mouth edges from a face image using eigenfeatures and multilayer perceptions. Pattern Recognition 34, 2459–2466 (2001)
Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transaction on Pattern Analysis And Machine Intelligence 13(6) (1991)
Liew, A.W.-C., Yan, H.: An Adaptive Spatial Fuzzy Clustering Algorithm for 3-D MR Image Segmentation. IEEE Transaction on Medical Imaging 22(9) (2003)
Navon, E., Miller, O., Averbuch, A.: Color image segmentation based on adaptive local thresholds. Image and Vision Computing 23, 69–85 (2005)
Kollokian, V.: Performance analysis of automatic techniques for tissue classification in MRI of the human brain, Master’s thesis, Concordia University, Montreal, Canada (1996)
Kwan, R.-S., Evans, A., Pike, G.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imaging 18(11), 1085–1097 (1999)
Niessen, W., Vincken, K., Weickert, J., Haar Romeny, B., Viergever, M.: Multiscale segmentation of threedimensional MR brain images. Internat. J. Comput. Vision 31(2/3), 185–202 (1999)
Nowak, R.: Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Process. 8(10), 1408–1419 (1999)
Otsu, N.: A threshold selection method from graylevel histograms. IEEE Trans. Systems Man Cybernet. 9(1), 62–66 (1979)
Pal, N., Pal, S.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)
Pham, D., Xu, C., Prince, J.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–337 (2000)
Pizurica, A.: Image denoising using wavelets and spatial context modeling. Ph.D. thesis, Ghent University, Gent, Belgium (2002)
Pizurica, A., Philips, W., Lemahieu, I., Acheroy, M.: A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans. Med. Imaging 22(3), 323–331 (2003)
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Kong, J., Zhang, J., Lu, Y., Wang, J., Zhou, Y. (2005). A Novel Approach for Adaptive Unsupervised Segmentation of MRI Brain Images. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_94
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DOI: https://doi.org/10.1007/11579427_94
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