Abstract
This paper presents an application of a self-organizing map (SOM) model based on the image intensity gradient for the reconstruction of cerebral cortex from MR images. The cerebral cortex reconstruction is important for many brain science or medicine related researches. However, it is difficult to extract deep cortical folds. In our method, we apply the SOM model based on the image intensity gradient to deform the easily extracted white matter surface and extract the cortical surface. The intensity gradient vectors are calculated according to the intensities of image data. Thus the proper cortical surface can be extracted from the image information itself but not artificial features. The simulations on T1-weighted MR images show that the proposed method is robust to reconstruct the cerebral cortex.
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Miller, M.I., Hosakere, M., Barker, A.R., Priebe, C.E., Lee, N., Ratnanather, J.T., Wang, L., Gado, M., Morris, J.C., Csernansky, J.G.: Labeled Cortical Mantle Distance Maps of the Cingulate Quantify Differences between Dementia of the Alzheimer Type and Healthy Aging. Proc. Natl. Acad. Sci. 100, 15172–15177 (2003)
Chung, M.K., Robbins, S.M., Dalton, K.M., Davidson, R.J., Alexander, A.L., Evans, A.C.: Cortical Thickness Analysis in Autism with Heat Kernel Smoothing. Neuroimage 25, 1256–1265 (2005)
MacDonald, D., Kabani, N., Avis, D., Evans, A.C.: Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI. NeuroImage 12, 340–356 (2000)
Barta, P., Miller, M.I., Qiu, A.: A Stochastic Model for Studying the Laminar Structure of Cortex from MRI. IEEE Trans. Med. Imaging 24, 728–742 (2005)
Xu, C., Pham, D.L., Rettmann, M.E., Yu, D.N., Prince, J.L.: Reconstruction of the Human Cerebral Cortex from Magnetic Resonance Images. IEEE Trans. Med. Imaging 18, 467–480 (1999)
Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Trans. Image processing 7, 359–369 (1998)
Chuang, C.H., Cheng, P.E., Liou, M., Liou, C.Y., Kuo, Y.T.: Application of Self-Organizing Map (SOM) for Cerebral Cortex Reconstruction. International Journal of Computational Intelligence Research 3, 26–30 (2007)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Liou, C.Y., Tai, W.P.: Conformal Self-Organization for Continuity on a Feature Map. Neural Networks 12, 893–905 (1999)
Liou, C.Y., Tai, W.P.: Conformality in the Self-Organization Network. Artificial Intelligence 116, 265–286 (2000)
Su, H.-R., Liou, M., Cheng, P.E., Aston, J.A.D., Chuang, C.H.: MR Image Segmentation Using Wavelet Analysis Techniques. Neuroimage 26(1) (2005), Human Brain Map** 2005 Abstract
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Chuang, CH., Liou, JW., Cheng, P.E., Liou, M., Liou, CY. (2008). Intensity Gradient Self-organizing Map for Cerebral Cortex Reconstruction. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_39
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DOI: https://doi.org/10.1007/978-3-540-69158-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
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