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系統識別號 U0026-0812200911360197
論文名稱(中文) 水楊酸刺激鼠腦模型之聽覺神經細胞辨識與聚類分析
論文名稱(英文) The Identification and Clustering Analysis of Auditory Neurons for Salicylate-Induced Rat Model
校院名稱 成功大學
系所名稱(中) 醫學工程研究所碩博士班
系所名稱(英) Institute of Biomedical Engineering
學年度 93
學期 2
出版年 94
研究生(中文) 陳俐卉
研究生(英文) Li-hui Chen
學號 p8692406
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 口試委員-林灶生
口試委員-李宗南
指導教授-鄭國順
口試委員-孫永年
口試委員-何裕琨
中文關鍵字 類神經網路  Fos蛋白  水楊酸刺激鼠腦模型  支持向量聚類法 
英文關鍵字 Neural network  Fos-protein  Salicylated-induced rat model  Support vector clustering 
學科別分類
中文摘要   水楊酸刺激鼠腦模型為耳鳴機制研究中的一種以藥物刺激誘發實驗性耳鳴研究之動物模型,由於耳鳴成因非常多樣性,因此關於耳鳴發生的機制至今並沒有突破性的進展。經由水楊酸刺激鼠腦,將引起鼠腦內聽覺神經傳導路徑中各部位的神經細胞核活化,產生不同程度的Fos蛋白分佈,因此可從活化後Fos蛋白的數量與分佈中,研究水楊酸導致大白鼠產生實驗性耳鳴的機制。本研究中,為克服人工辨識的限制與缺點,故利用類神經網路之自我訓練特性,建構一神經細胞核自動辨識系統,以Radial Basis Function Neural Network為網路架構,經訓練與測試階段正確率可達98%,不僅提升辨識速度,結果也較為客觀。此外,本系統也應用Support Vector Clustering (支持向量聚類法) 量化神經細胞核所誘發的Fos蛋白之分佈。傳統分析上僅以Fos蛋白之數量或密度作比較,而忽略其在分佈中的聚類現象,支持向量聚類法可將空間分佈中的Fos蛋白作可能的聚類,以期提供生理學家另一個參考的依據。從本研究之聚類分析結果發現,經由水楊酸刺激之Fos分佈之纇別與面積與控制組之Fos分佈有明顯之不同,不過仍有待進一步生理研究與分析之驗證。
英文摘要   Salicylate-induced rat model is one of the animal models for tinnitus study. Since the mechanisms for the tinnitus are varied a lot, there is no break-through progress in related studies. Through the study of the salicylate-induced rat model, the conduction pathway for the auditory neurons in the brain may be activated. These activated neurons may then be fos-labeled for analysis. From the identification and distribution analysis, some mechanism of tinnitus may be revealed from the Salicylate-induced rat model study. In this study, in order to overcome the disadvantages of the manual identification of auditory neurons, a radial basis function neural network for automatic identification is developed due to its features of easy training and learning. From the experimental results, the recognition rate is demonstrated to be as high as 98%. Not only the recognition rate is improved, but also it is very objective in analysis. In addition, a support vector clustering is applied to neurons distribution analysis. Traditionally, the distribution is only characterized in number or density of neurons. Using the support vector machine, the clustering feature may be obtained as another possible parameter for analysis. Base on the clustering analysis, it is found that the cluster number and distribution area for the Salicylated-induced fos-labeled neurons are very different from those of controlled group. However, it needs to be further investigated in physiology.
論文目次 CHINESE ABSTRACT …………………………………………………………………………II
ABSTRACT ……………………………………………………………………………………III
ACKNOWLEDGMENTS …………………………………………………………………………V
LIST OF TABLES…………………………………………………………………………… VII
LIST OF FIGURES………………………………………………………………………… VIII


Chapter 1 Introduction …………………………………………………………………… 1
1.1 Background ………………………………………………………………………… 1
1.1.1 Tinnitus ……………………………………………… …………………………… 1
1.1.2 Animal Model for Salicylate-Induced Tinnitus …………………………… 2
1.1.3 Fos as a Neural Activity Marker ……………………………………………… 4
1.2 Fos Recognition Methods ………………………………………………………… 4
1.2.1 Manual Identification …………………………………………………………… 4
1.2.2 Knowledge-based Method ………………………………………………………… 5
1.2.3 Artificial Neural Network Method …………………………………………… 6
1.3 Clustering Analysis …………………………………………………………… 8
1.3.1 Spatial Point Analysis ………………………………………………………… 10
1.3.2 Fractal Dimension Analysis …………………………………………………… 10
1.4 Motivations and Purposes ……………………………………………………… 11
1.5 The Organization of the Thesis ……………………………………………… 11

Chapter 2 Materials and Methods ………………………………………………………… 13
2.1 System Description …………………………………………………………………… 13
2.1.1 System Environment ………………………………………………………………… 13
2.2.2 Research Framework ………………………………………………………………… 13
2.2 Image Acquisition ……………………………………………………………………… 14
2.3 Image Preprocessing …………………………………………………………………… 15
2.4 Pattern Recognition …………………………………………………………………… 16
2.4.1 Features Extraction ………………………………………………………………… 16
2.4.2 Radial Basis Function Neural Network (RBFNN) for Classification ……… 16
2.4.3 The Performance Evaluation …………………………………………………………19
2.5 Support Vector Clustering (SVC) for Distribution Analysis ………………… 20
2.5.1 Introduction ………………………………………………………………………… 20
2.5.2 Theory ………………………………………………………………………………… 21

Chapter 3 Experiments and Results ……………………………………………………… 26
3.1 Image Processing ……………………………………………………………………… 26
3.2 Feature Quantification………………………………………………………………… 27
3.3 Recognition Results …………………………………………………………………… 29
3.4 Clustering analysis …………………………………………………………………… 35
3.4.1 Simulations …………………………………………………………………………… 35
3.4.2 Applications ………………………………………………………………………… 41

Chapter 4 Discussion and Conclusion …………………………………………………… 47

Reference…………………………………………………… 50
參考文獻 [ 1 ] J. W. Hazell and P. J. Jastreboff, “Tinnitus I auditory mechanisms: a model for tinnitus and hearing impairment,” Journal of Otolaryngology, vol. 19, pp. 1-5, 1990.
[ 2 ] P. J. Jastreboff and C. T. Sasaki, “An animal model of tinnitus: a decade of development,” American Journal of Otology, vol. 15, pp. 19-27, 1994.
[ 3 ] Http://physics.tmmu.com.cn/wskt/hear/www/html/player/photo.htm.
[ 4 ] Y. C. Chu, Origins of Salicylate-induced Tinnitus in the Auditory Brainstem of Rats Revealed by Fos Immunohistochemistry, Master thesis, Institute of Physiology, National Cheng Kung University, 2003.
[ 5 ] J. L. Wu, T. W. Chiu, and P. W. Poon, “Differential changes in fos-immunreactivity at the auditory brainstem after chronic injections of Salicylate in rats,” Hearing Research, vol. 176, pp. 80-93, 2003.
[ 6 ] S. M. Sagar and F. P. Shape, “Early response genes as markers of neuronal activity and growth factor action,” Advanced Neurology, vol. 59, pp. 273-284, 1993.
[ 7 ] D. Carretta, et al., “c-fos expression in the auditory pathways related to the significance of acoustic signals in rats performing a sensory-motor task,” Science, vol. 841, pp. 170-183, 1999.
[ 8 ] Yi-Jung Wang, The Identification and Distribution Analysis for Nuclei of Hearing Neurons, Master thesis, Institute of Biomedical Engineering, National Cheng Kung University, 2002.
[ 9 ] Y. Hu, R. Veltri, G. O’Dowd, C. Miller, R. Hurst, and B. Bonner, “A comparison of neural network and fuzzy c-means methods in bladder cancer cell classification,” IEEE World Congress on Computational Intelligence, pp. 3461-3466, 1994.
[10] L. Wei, X. Jianhong, and E. M. Tzanakou, “A computational intelligence system for cell classification,” Proc. IEEE International Conference on Information Technology Applications in Biomedicine, pp.105-109, 1998.
[11] D. Milenovic, L. Stoiljkovic, and N. Stojanovic, “Cell classification for diagnostic of reactive histocytic hyperplasia using neural networks,” The 9th Mediterranean Conference on Electrotechnics, vol. 2, pp. 1466-1470, 1998.
[12] A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, vol. 30, pp. 1145-1159, 1997.
[13] P. J. Diggle, Statistical Analysis of Spatial Point Patterns, Academic Press: New York, 1983.
[14] H. O. Peitgen, D. Saupe, and L. Yunker, Fractal for the Classroom: Strategic Activities Volume One, S. Verlag: New York, 1991.
[15] N. Sarkar and B. B. Chaudhuri, “An efficient differential box-counting approach to compute fractal dimension of images,” IEEE Transactions on Systems, Man and Cybernetics, vol. 24, pp. 115-120, 1994.
[16] N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, pp. 62-66, 1979.
[17] D. S. Broomhead and D. Lowe, “Multivariate functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321-355, 1988.
[18] J. Moody and C. Darken, “Fast learning in networks of locally-tuned processing units,” Neural Computation, vol. 1(2), pp. 281-294, 1989.
[19] M. R. Anderberg, Cluster Analysis for Applications, Academic: New York, 1973.
[20] J. MacQueen, L. M. LeCam, and J. Neyman, “Some methods for classification and analysis of multivariate observations,” Proceedings of the Fifth Berkeley Symposium on Mathematics, Statistics, and Probability, pp. 281-297, 1967.
[21] Vance Faber, “Clustering and the continuous k-means algorithm,” Los Alamos Science, pp. 138-144, 1994.
[22] B. D. Ripley, Spatial Statistics, John Wiley & Sons, 1981.
[23] A. Ben-Hur, D. Hon, H. T. Siegelmann, and V. N. Vapnik, “A support vector clustering method,” Proceedings of International Conference on Pattern Recognition, vol. 2, pp. 728-732, 2000.
[24] A. Ben-Hur, D. Hon, H. T. Siegelmann, and V. N. Vapnik, “Support vector clustering,” Journal of Machine Learning Research, vol. 2, pp. 125-137, 2001.
[25] D. Tax, and R. Duin, “Support vector data description,” Machine Learning, vol. 54, pp. 45-66, 2004.
[26] P. Y. Hao, Fuzzy Decision Model Using Support Vector Learning – A Kernel Function Based Approach, Doctoral dissertation, Department of Computer Science and Information Engineering, National Cheng Kung University, 2003.
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