Abstract
In this paper, we present some of our current studies on how human brain structures are influenced by cognitive disorders occurred from various neurological and psychiatric diseases based on magnetic resonance imaging (MRI). We first give a brief introduction about computational neuroanatomy, which is the basis of these studies. In Section 2, several novel methods on segmentations of brain tissue and anatomical substructures were presented. Section 3 presented some studies on brain image registration, which plays a core role in computational neuroanatomy. Shape analysis of substructures, cerebral cortical thickness and complexity was presented in Section 4. Finally, some prospects and future research directions in this field are also given.
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Keywords
- Attention Deficit Hyperactivity Disorder
- Markov Random Field
- Cognitive Disorder
- Active Contour Model
- Digital Human Modeling
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References
Csernansky, J.G., Wang, L., Joshi, S.C., Ratnanather, J.T., Miller, M.I.: Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change. NeuroImage 23 (Suppl 1), 56–68 (2004)
Thompson, P.M., Toga, A.W.: A Framework for Computational Anatomy. Computing and Visualization in Science 5, 13–34 (2002)
Ashburner, J.: Computational neuroanatomy. Ph.D. thesis, University College London (2000)
Zhu, C.Z., Jiang, T.Z.: Multi-context Fuzzy Clustering for Separation of Brain Tissues in MR Images. NeuroImage 18, 685–696 (2003)
Yang, F.G., Jiang, T.Z.: Pixon-Based Image Segmentation With Markov Random Fields. IEEE Trans. Imag. Proc. 12, 1552–1559 (2003)
Zhu, W.L., Jiang, T.Z., Li., X.B.: Segmentation of Brain MR Images Using J-Divergence Based Active Contour Models. In: Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery, vol. II, Springer, Heidelberg (2007)
Fan, Y., Jiang, T.Z., Evans, D.J.: Volumetric Segmentation of Brain Images Using Parallel Genetic Algorithm. IEEE Trans. Med. Imaging 21, 904–909 (2002)
Tang, S.Y., Jiang, T.Z.: Non-rigid Registration of Medical Image by Linear Singular Blending Techniques. Pattern Recogn Lett. 25, 399–405 (2004)
Wang, J.Z., Jiang, T.Z.: Nonrigid Registration of Brain MRI Using NURBS. Pattern Recognition Letters 28(2), 214–223 (2007)
Wang, J.Z., Jiang, T.Z., Cao, Q.J., Wang, Y.F.: Characterizing Anatomical Differences in Boys with Attention Deficit Hyperactivity Disorder Using Deformation Based Morphometry. American Journal of Neuroradiology 28(3), 543–547 (2007)
Zhu, L.T., Jiang, T.Z.: Parameterization of 3D Brain Structures for Statistical Shape Analysis. In: proceedings of SPIE Medical Imaging 2004, California, USA , 2004, pp. 14–17 (2004)
Li, S.Y., Shi, F., Jiang, T.Z. et al.: Hippocampal shape analysis of Alzheimer’s disease based on machine learning methods, American Journal of Neuroradiology (in press, 2007)
Hariri, A.R., Weinberger, D.R.: Imaging genomics. Br Med Bull. 65, 259–270 (2003)
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Jiang, T., Shi, F., Zhu, W., Li, S., Li, X. (2007). Shape Analysis of Human Brain with Cognitive Disorders. In: Duffy, V.G. (eds) Digital Human Modeling. ICDHM 2007. Lecture Notes in Computer Science, vol 4561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73321-8_47
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DOI: https://doi.org/10.1007/978-3-540-73321-8_47
Publisher Name: Springer, Berlin, Heidelberg
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