Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/92963
標題: MAP2 Antibody Staining Rat Brain Tissue Image Based Stroke Stage Diagnostic Method
以 MAP2 抗體染色之老鼠腦組織影像為基礎之中風診斷方法
作者: 楊智婷
Chih-Ting Yang
關鍵字: ischemic stroke
stroke stage
image recognition
medical imaging
缺血性中風
中風階段
影像辨識
醫學影像
引用: J. Mackay and G. A. Mensah, 'The atlas of heart disease and stroke,' The atlas of heart disease and stroke, 2004. U. Dirnagl, C. Iadecola, and M. A. Moskowitz, 'Pathobiology of ischaemic stroke: an integrated view,' Trends in Neurosciences, vol. 22, pp. 391-397, Sep 1999. C. D. A. Wolfe, 'The impact of stroke,' British Medical Bulletin, vol. 56, pp. 275-286, 2000. WHO:Thetop10causesofdeath.Available: http://www.who.int/mediacentre/factsheets/fs310/en/ A. S. Go, D. Mozaffarian, V. L. Roger, E. J. Benjamin, J. D. Berry, M. J. Blaha, et al., 'Heart Disease and Stroke Statistics—2014 Update: A Report From the American Heart Association,' Circulation, vol. 129, pp. e28-e292, December 18 2013. P. Mergenthaler and A. Meisel, 'Do stroke models model stroke?,' Disease Models & Mechanisms, vol. 5, pp. 718-725, 2012. K. M. Sicard and M. Fisher, 'Animal models of focal brain ischemia,' Exp Transl Stroke Med, vol. 1, pp. 1-7, 2009. J. B. Casals, N. C. Pieri, M. L. Feitosa, A. C. Ercolin, K. C. Roballo, R. S. Barreto, et al., 'The use of animal models for stroke research: a review,' Comparative medicine, vol. 61, p. 305, 2011. A. Caceres, L. Binder, M. Payne, P. Bender, L. Rebhun, and O. Steward, 'Differential subcellular localization of tubulin and the microtubule-associated protein MAP2 in brain tissue as revealed by immunocytochemistry with monoclonal hybridoma antibodies,' The Journal of Neuroscience, vol. 4, pp. 394-410, 1984. K. Kitagawa, M. Matsumoto, M. Niinobe, K. Mikoshiba, R. Hata, H. Ueda, et al., 'Microtubule-associated protein 2 as a sensitive marker for cerebral ischemic damage—Immunohistochemical investigation of dendritic damage,' Neuroscience, vol. 31, pp. 401-411, 1989. K. M. Marsden, T. Doll, J. Ferralli, F. Botteri, and A. Matus, 'Transgenic Expression of Embryonic MAP2 in Adult Mouse Brain: Implications for Neuronal Polarization,' The Journal of Neuroscience, vol. 16, pp. 3265-3273, May 15 1996. C.-C. Shen, Y.-C. Yang, M.-T. Chiao, W.-Y. Cheng, Y.-S. Tsuei, and J.-L. Ko, 'Characterization of endogenous neural progenitor cells after experimental ischemic stroke,' Current neurovascular research, vol. 7, pp. 6-14, 2010. R. M. E. Chalmers-Redman, A. D. Fraser, W. Y. H. Ju, J. Wadia, N. A. Tatton, and W. G. Tatton, 'Chapter 1 Mechanisms of Nerve Cell Death: Apoptosis or Necrosis After Cerebral Ischaemia,' in International Review of Neurobiology. vol. Volume 40, A. R. Green and J. C. Alan, Eds., ed: Academic Press, 1996, pp. 1-25. A. Y. Shih, B. Friedman, P. J. Drew, P. S. Tsai, P. D. Lyden, and D. Kleinfeld, 'Active dilation of penetrating arterioles restores red blood cell flux to penumbral neocortex after focal stroke,' J Cereb Blood Flow Metab, vol. 29, pp. 738-751, Apr 2009. V. E. O'Collins, M. R. Macleod, G. A. Donnan, L. L. Horky, B. H. van der Worp, and D. W. Howells, '1,026 Experimental treatments in acute stroke,' Annals of Neurology, vol. 59, pp. 467-477, 2006. K. G. Shojania, E. C. Burton, K. M. McDonald, and L. Goldman, 'Changes in rates of autopsy-detected diagnostic errors over time: A systematic review,' JAMA, vol. 289, pp. 2849-2856, 2003. G. D. Schiff, O. Hasan, S. Kim, and et al., 'Diagnostic error in medicine: Analysis of 583 physician-reported errors,' Archives of Internal Medicine, vol. 169, pp. 1881-1887, 2009. H. J. Michtalik, H. Yeh, P. J. Pronovost, and D. J. Brotman, 'Impact of attending physician workload on patient care: A survey of hospitalists,' JAMA Internal Medicine, vol. 173, pp. 375-377, 2013. R. C. Gonzales and R. E. Woods, 'Digital Image Processing, 2-nd Edition,' ed: Prentice Hall, 2002. M. H. Bharati, J. J. Liu, and J. F. MacGregor, 'Image texture analysis: methods and comparisons,' Chemometrics and Intelligent Laboratory Systems, vol. 72, pp. 57-71, 2004. 'IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology,' IEEE Std 610.4-1990, 1990. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, 'Textural Features for Image Classification,' Systems, Man and Cybernetics, IEEE Transactions on, vol. SMC-3, pp. 610-621, 1973. M. Tuceryan and A. K. Jain, 'Texture analysis,' The handbook of pattern recognition and computer vision, vol. 2, pp. 207-248, 1998. T. M. Lehmann, M. O. Güld, T. Deselaers, D. Keysers, H. Schubert, K. Spitzer, et al., 'Automatic categorization of medical images for content-based retrieval and data mining,' Computerized Medical Imaging and Graphics, vol. 29, pp. 143-155, 2005. C. A. Murthy and N. Chowdhury, 'In search of optimal clusters using genetic algorithms,' Pattern Recognition Letters, vol. 17, pp. 825-832, 1996. S. Bandyopadhyay and U. Maulik, 'An evolutionary technique based on K-means algorithm for optimal clustering in RN,' Information Sciences, vol. 146, pp. 221-237, 2002. K. Krishna and M. N. Murty, 'Genetic K-means algorithm,' Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 29, pp. 433-439, 1999. W. Kwedlo and P. Iwanowicz, 'Using Genetic Algorithm for Selection of Initial Cluster Centers for the K-Means Method,' in Artifical Intelligence and Soft Computing. vol. 6114, L. Rutkowski, R. Scherer, R. Tadeusiewicz, L. Zadeh, and J. Zurada, Eds., ed: Springer Berlin Heidelberg, 2010, pp. 165-172. H. Tamura, S. Mori, and T. Yamawaki, 'Textural Features Corresponding to Visual Perception,' Systems, Man and Cybernetics, IEEE Transactions on, vol. 8, pp. 460-473, 1978. J. H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence: U Michigan Press, 1975. K.-F. Man, K. S. TANG, and S. Kwong, Genetic Algorithms: Concepts and Designs, Avec disquette vol. 1: Springer, 1999. J. MacQueen, 'Some methods for classification and analysis of multivariate observations,' in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, pp. 281-297. A. R. Smith, 'Color gamut transform pairs,' SIGGRAPH Comput. Graph., vol. 12, pp. 12-19, 1978. N. Dalal and B. Triggs, 'Histograms of oriented gradients for human detection,' in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 886-893 vol. 1. V. Raghavan, P. Bollmann, and G. S. Jung, 'A critical investigation of recall and precision as measures of retrieval system performance,' ACM Transactions on Information Systems (TOIS), vol. 7, pp. 205-229, 1989. J. Davis and M. Goadrich, 'The relationship between Precision-Recall and ROC curves,' in Proceedings of the 23rd international conference on Machine learning, 2006, pp. 233-240. R. Kohavi, 'A study of cross-validation and bootstrap for accuracy estimation and model selection,' in Proceedings of the 14th International Conference on Artificial Intelligence (IJCAI), San Mateo, CA, Morgan Kaufmann, 1995, pp. 1137–1143. C.-C. Shen, C.-H. Lin, Y.-C. Yang, M.-T. Chiao, W.-Y. Cheng, and J.-L. Ko, 'Intravenous implanted neural stem cells migrate to injury site, reduce infarct volume, and improve behavior after cerebral ischemia,' Current neurovascular research, vol. 7, pp. 167-179, 2010. J. Aronowski, R. Strong, and J. Grotta, 'Combined Neuroprotection and Reperfusion Therapy for Stroke Effect of Lubeluzole and Diaspirin Cross-Linked Hemoglobin in Experimental Focal Ischemia,' Stroke, vol. 27, pp. 1571-1577, 1996. J. Chen, Y. Li, R. Zhang, M. Katakowski, S. C. Gautam, Y. Xu, et al., 'Combination therapy of stroke in rats with a nitric oxide donor and human bone marrow stromal cells enhances angiogenesis and neurogenesis,' Brain research, vol. 1005, pp. 21-28, 2004. C. Culmsee, V. Junker, W. Kremers, S. Thal, N. Plesnila, and J. Krieglstein, 'Combination Therapy in Ischemic Stroke: Synergistic Neuroprotective Effects of Memantine and Clenbuterol,' Stroke, vol. 35, pp. 1197-1202, 2004. L. Zhang, Z. G. Zhang, G. L. Ding, Q. Jiang, X. Liu, H. Meng, et al., 'Multitargeted effects of statin-enhanced thrombolytic therapy for stroke with recombinant human tissue-type plasminogen activator in the rat,' Circulation, vol. 112, pp. 3486-3494, 2005. R. J. Adams, G. Albers, M. J. Alberts, O. Benavente, K. Furie, L. B. Goldstein, et al., 'Update to the AHA/ASA recommendations for the prevention of stroke in patients with stroke and transient ischemic attack,' Stroke, vol. 39, pp. 1647-1652, 2008.
摘要: Stroke, also known as cerebral vascular accident, has rank second on the top ten causes of death worldwide in the past decade. Interrupted blood supply due to blood vessel blockage or sudden burst of blood vessels can cause brain damage and thereby result in long-term effects or even death. Out of all stroke incidents, blood vessel blockage, which is referred to as ischemic stroke accounts for approximately 87%. Therefore, there are increasing studies on a better understanding of this disease and on developing improved treatment. Animal models are used to better understand stroke by simulating the pathophysiological changes in human stroke, rodents especially rats are the most commonly used stroke models. In this research, images from animal models of ischemic stroke carried out in rats are the basis of the proposed method. In this research, an automatic stroke diagnostic method was proposed. The method firstly extracts image features by using gray-level co-occurrence matrix (GLCM) and Tamura. Then the method trains these features by using genetic algorithm and k-means clustering algorithm to obtain the stroke diagnosis model. Using this model, we can recognize the stroke stage of testing rats. The overall experimental results indicate that the proposed method achieves good performance in recognition of different stroke stage images. On top of that, a proofed effective stroke treatment carried out in experiment is used to verify the proposed method, the experimental results also demonstrates well performance.
腦中風(stroke),又稱為腦血管意外(cerebral vascular accident),近年來在全球十大死因中高居第二。中風起因於腦血管阻塞或爆裂所造成的腦部破壞,使患者留下長期後遺症甚至是死亡。腦血管阻塞又稱為缺血性中風(ischemic stroke),在所有的中風事件中,其發生機率約略為 87%。因此,現今已有更多的研究投入於更深入了解中風疾病,並且發展更妥善的治療方法。在醫學實驗中 動物模型常被用於模擬疾病在人體可能產生的病理生理變化。,而在腦中風的動物實驗中,經常使用囓齒動物特別是老鼠進行研究。本研究的實驗影像即為以老鼠為主之缺血性腦中風動物模型。本研究所提出的自動化中風階段診斷方法是基於影像處理的方法。此方法首先經由灰階共現矩陣(GLCM)及 Tamura 方法針對不同階段中風影像擷取影像特徵 將影像特徵值透過基因演算法結合 K-means 分群演算法訓練中風診斷模型,,利用不同階段中風影像的不同特徵作為判斷中風嚴重程度的依據。實驗結果顯示,本方法在診斷中風階段有良好的結果,並且透過已知有效的治療中風藥物也能驗證本方法的有效性。
URI: http://hdl.handle.net/11455/92963
其他識別: U0005-2105201514294100
文章公開時間: 2018-07-15
Appears in Collections:資訊管理學系

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