Please use this identifier to cite or link to this item:
標題: SPM的分類方式及使用IBASPM手動分割加強SPM分割病理性大腦
Segmentation of SPM and Using IBASPM manual segment to enhance for SPM in Pathological Brains
作者: 李宜峰
Lee, Yi-Fong
關鍵字: 統計參數映射;Statistical Parametric Mapping;腦部結構型態學地圖集;Individual Brain Atlases using Statistical Parametric Mapping software
出版社: 電機工程學系所
引用: [1]. R.J. Adler (1981). The geometry of random fields. Wiley New York. [2]. C. Büchel, R.S.J. Wise, C.J. Mummery, J.-B. Poline and K.J. Friston (1996). Nonlinear regression in parametric activation studies. NeuroImage, 4:60-66. [3]. K.J. Friston, C.D. Frith, P.F. Liddle and R.S.J. Frackowiak (1991). Comparing functional (PET) images: the assessment of significant change. Journal of Cerebral Blood Flow and Metabolism, 11:690-699. [4]. K.J. Friston, K.J. Worsley, R.S.J. Frackowiak, J.C. Mazziotta and A.C. Evans (1994). Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1:214-220. [5]. K.J. Friston, A.P. Holmes, K.J. Worsley, J.-B. Poline, C.D. Frith and R.S.J. Frackowiak (1995). Statistical Parametric Maps in functional imaging: A general linear approach. Human Brain Mapping, 2:189-210. [6]. K.J. Friston (1997). Testing for anatomical specified regional effects. Human Brain Mapping, 5:133-136. [7]. K.J. Worsley, A.C. Evans, S. Marrett and P. Neelin (1992). A three-dimensional statistical analysis for rCBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism, 12:900-918. [8]. K.J. Worsley, S. Marrett, P. Neelin, A.C. Vandal, K.J. Friston and A.C. Evans (1996). A unified statistical approach or determining significant signals in images of cerebral activation. Human Brain Mapping, 4:58-73. [9]. Collins D L, Holmes C, Peters T, Evans A. Automatic 3D Model-Based Neuroanatomical Segmentation. Human Brain Mapping. Vol.3: 190-208.1995. [10]. Collins D. , A. Zijdenbos, and A. Evans. Automatic Volume Estimation of Gross Cerebral Structures. 4th International Conference on Functional Mapping of the Human Brain, Montreal, June 1998. Organization for Human Brain Mapping. abstract no. 702. [11]. Collins D. L., A. P. Zijdenbos, W. F. C. Barré, and A. C. Evans, ``ANIMAL+INSECT: Inproved cortical structure segmentation,'' in Proc. of the Annual Symposium on Information Processing in Medical Imaging (A. Kuba, M. Samal, and A. Todd-Pokropek, eds.), vol. 1613 of LNCS, pp. 210-223, Springer, 1999. [12]. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.Neuron. 2002 Jan 31;33. (3):341-55. [13]. Fischl B, Van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V,Makris N, Rosen B, Dale AM. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004 Jan; 14(1):11-22. [14]. Maldjian JA,Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003 Jul;19(3):1233-9. [15]. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N. Automated anatomical labelling of activations in spm using a macroscopic anatomical parcellation of the MNI MRI single subject brain. Neuroimage 15: 273-289. 2002. [16]. Mazziotta J. , Toga A. , Evans A. , Fox P. and Lancaster. J. A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping. NeuroImage, 2(2):89-101, 1995. [17]. Huang, J., Smola, A., Gretton, A., Borgwardt, K., & Sch¨olkopf, B. (2006a). Correcting Sample Selection Bias by Unlabeled Data. Technical Report CS-2006-44). University of Waterloo. [18]. Arnold JB, Liow JS, Schaper KA et al. (2001) Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effect. [19]. Ashburner J, Andersson J, Friston KJ (2000) Image registration using a symmetric prior - in three-dimensions. [20]. Pham DL, Prince JL (1999) Adaptive fuzzy segmentation of magnetic resonance images. [21]. Hellier P, Ashburner J, Corouge I et al. (2002) Inter subject registration of functional and anatomical data using SPM. In Proc Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 2489 of Lecture Notes in Computer Science. Springer-Verlag, Berlin and Heidelberg. [22]. Xiaohua C, Brady M, Rueckert D (2004) Simultaneous segmentation and registration for medical image. In Proc Medical Image Computing and Computer-Assisted Intervention (MICCAI), Barillot C, Haynor DR, Hellier P (eds) vol. 3216 of Lecture Notes in Computer Science.

Statistical parametric mapping (SPM) is the professional image process software, which is used for MR image processing. The brain MR image can be feed into SPM software for further processing and investigation. The software includes many mathematic models and hundreds of functions therefore not easy to understand. User usually just carry out the standard procedures and does not know the detail of how it works. This study is trying to find out the mathematical methods which are developed for the use of SPM segmentation. Besides, Individual Brain Atlases using Statistical Parametric Mapping (IBASPM) software is an atlas-based method for automatic segmentation of brain structures is also studied. Through the IBASPM manual segment, we can enhance pathological brains for SPM.
其他識別: U0005-1002201012492300
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.