Abstract
A neuro-fuzzy clustering framework has been presented for a meaningful segmentation of Magnetic Resonance medical images. MR imaging provides detail soft tissue descriptions of the target body object and it has immense importance in today’s non-invasive therapeutic planning and diagnosis methods. The unlabeled image data has been classified using fuzzy c-means approach and then the data has been used for training of an Elman neural network. The trained neural net is then used as a ready-made tool for MRI segmentation.
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Ray, D., Dutta Majumder, D.: Studies on Some multimodal medical image registration approaches for diagnostics and therapeutic planning: With some case studies. Science Letters, NAS 28(5&6), 135–154 (2005)
Sutton, M.A., Bezdek, J.C., Cahoon, T.C.: Image Segmentation by Fuzzy Clustering: Methods and Issues. In: Bankman, I.N. (ed.) Handbook of Medical Imaging-Processing and Analysis, pp. 87–106. Academic Press, London (2000)
Bezdek, J.C., Hathaway, R.J., Sabin, M.J., Tucker, W.T.: Convergence theory for fuzzy C-means: counter examples and repairs. IEEE Trans. Syst. Man Cybern. 17873–17877 (1987)
Elman, J.L.: Finding structure in time. Cognitive Science 14, 179–221 (1990)
Hagan, M.T., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Transaction on Neural Networks 5(6), 989–993 (1994)
Powell, M.J.D.: Radial basis functions for multivariable interpolation: a review. In: Algorithms for Approximation, pp. 143–167. Clarendon Press, Oxford (1987)
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© 2009 Springer-Verlag Berlin Heidelberg
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Ray, D., Majumder, D.D. (2009). Development of a Neuro-fuzzy MR Image Segmentation Approach Using Fuzzy C-Means and Recurrent Neural Network. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_21
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DOI: https://doi.org/10.1007/978-3-642-11164-8_21
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