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系統識別號 U0026-0706201215140200
論文名稱(中文) 多目標小型生物追蹤演算法的發展
論文名稱(英文) Development of a Multi-Target Tracking Algorithm for Small Organisms
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 100
學期 2
出版年 101
研究生(中文) 葉郁欣
研究生(英文) Yu-Sing Yeh
學號 N28941468
學位類別 博士
語文別 英文
論文頁數 104頁
口試委員 指導教授-羅錦興
召集委員-黃廣志
口試委員-楊順聰
口試委員-廖斌毅
口試委員-李彥杰
口試委員-黃克穠
口試委員-任善隆
中文關鍵字 動物追蹤  定位演算法  碰撞演算法  影像處理 
英文關鍵字 multi-target tracking algorithm  paramecia  transpose  collide 
學科別分類
中文摘要 大部分微生物追蹤被用於其特性的研究,其中也有不少文獻旨於發展各種有效的量測草履蟲行為之方法。因此,為了分析與驗證大量的草履蟲行為量測數據,草履蟲行為量測演算法的發展也相形地愈發重要。另一方面,在生物醫學的領域中,監測實驗鼠行為之變化對於臨床實驗的結果也十分重要。而量測老鼠行為的方法,有些研究在老鼠身上外加顏色標記後使用影像系統,有些則使用紅外線偵測系統以進行動物行為的量測實驗。若是追蹤小型生物時能不使用顏色標記,則其行為量測除了能在更自然的情況下實驗,同時也能進行如互動行為量測此類的較複雜實驗。
對於一套小型生物行為量測系統而言,除了必要的準確性之外,簡易、方便、能被廣泛使用的特性能增加其功能性與可靠度。本篇論文發展了一套無需使用顏色標記的影像式之多目標小型動物追蹤演算法,此演算法包含了影像處理、定位演算法以及碰撞演算法,能依照不同的需求,正確的分辨、追蹤以及記錄實驗區中的小型動物之行為。由於考慮到不同動物實驗需要不同演算法的影像處理,此演算法是使用圖控軟體Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW)發展而成,可利用CCD攝影機搭配光學顯微鏡或者USB數位電子顯微鏡來完成小動物影像擷取的動作,並進一步地達成無需使用顏色標記的追蹤能力。
本論文所發展的多目標追蹤演算法能在小型生物發生錯位與碰撞運動時,持續地進行連續的軌跡追蹤。在草履蟲追蹤的部分,此演算法可以順利地追蹤草履蟲的行徑速度達到1.7mm/s,且可同時追蹤五隻草履蟲,並且將其移動資料記錄下來。本研究所設計的草履蟲追蹤每次持續6分鐘,在配合農藥馬拉松的生物毒性實驗中可得知於濃度1/4 LC50時,草履蟲的移動速度會提高約28% ~ 82%,而在濃度1/2 LC50時,草履蟲的移動速度會提高約93% ~ 171%。在老鼠追蹤的部分,在為時30分鐘的實驗中得到老鼠每分鐘的移動距離皆不長於1000cm、兩隻實驗鼠相距的範圍為0~64cm(0 cm為老鼠靠在一起;64 cm為實驗箱最大距離)、老鼠每秒運動的加速度範圍為-0.17~0.13 cm/s2,以及其的趨牆性為 35%~100% (接近100%為靠近牆圍;接近0%為靠近中心點)等實驗結果。
而在藉由本研究所設計的實驗,可以得知草履蟲的行為表現與農藥馬拉松的生物毒性濃度之關係,也可以追蹤並記錄小鼠的互動行為與趨牆性。因此,可以得證此論文所提出的系統不但優於傳統的行為量測系統,而且在小型生物行為的監測上,更為合適且有幫助。
英文摘要 Various investigations for tracking microorganisms were used to examine the characteristics of microorganism. Researchers have also developed various effective methods to measure the behavior of paramecium. The algorithm of microorganism behavior measurement is more and more important due to the analysis and verification of a large quantity of behavioral data.
To monitor the behaviors of mice before the decisions of further clinical experiments is an important process in biomedicine domain. Some studies of mice behaviors utilize video system with color marks and infrared beams system to measure the behavior of animal. If the behavioral tracking two mice without using any colored markers, then the experiment will be executed more naturally, and the acquired data can be used to measure the experimental anxiety of mice.
A system that can accurately measure the behavior of small motile organism (animal) is thus essential. It should be simple, user-friendly and widely adopted in small motile animal behavior research in addition to its performance power and reliability. This investigation develops a multi-target tracking algorithm for small motile organisms (animals). The algorithm can recognize, track and record the orbit of moving organisms inside a small experimental area. The positioning algorithm and the collision algorithm for processing the image of organisms and analyzing the traces of them are developed by a software tool, Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). It can be implemented by an optical microscope and a CCD camera or a USB optical camera microscope.
The multi-target tracking algorithm in this study can continue to track small motile organisms even if they are transposed or collide with each other. For the experiment of paramecia tracking, it can really track five paramecia and simultaneously yield meaningful data from the moving paramecia at a maximum speed of 1.7mm/s. The average speeds of paramecia, taken within 6 minutes right after the encounter of different malathion concentration in the mixture, 1/4 LC50 and 1/2 LC50,and it increased 28% ~ 82% and 93% ~ 171% respectively. In these experiments, the movement displacement limits lower than 1000 cm/min, the distance between two mice is 0~64 cm (0 cm is the distance when mice nestled up. 64 cm is the maximum distance of the experiment box.), the range of the acceleration of mice is -0.17~0.13 cm/s2, and the thigmotaxic of the walls, the distance between two mice, the range of the acceleration of mice, and the thigmotaxic of the walls, the distance from a mouse to the center of test box of mouse 1 is 35%~100% (100% is approach es the center of test wall. 0% is approach es the center of test box.), taken within 30 minutes right after two mice was put into the test box.
Therefore the malathion concentrations affects the locomotor behavior of paramecia significantly. And the interaction, moreover, the thigmotaxic of mice toward the walls can be track and record. To summarize, we demonstrated that the proposed system is superior to the traditional tracking systems, therefore it is useful and more suitable in small motile organism (animal) behavior monitoring.
論文目次 摘要 I
ABSTRACT III
致謝 VI
CONTENTS VII
LIST OF FIGURES VIII
Chapter 1 Introduction 1
Chapter 2 Methods 5
2.1 The multi-target tracking system 5
2.2 Image processing 10
2.2.1 Paramecia Images processing 14
2.2.2 Mice Images processing 33
2.3 Positioning algorithm 39
2.4 Collision algorithm 46
Chapter 3 Experiment and Results 55
3.1 Paramecia 55
3.1.1 System verification 55
3.1.2 Result of real multi-target tracking for paramecia 66
3.1.3 Result of animal interaction behavior tracking data analyzing 71
3.2 Mice 76
3.2.1 System verification 76
3-2-2 Result of animal interaction and behavior tracking system 79
3-2-3 Result of animal interaction behavior tracking data analyzing 83
Chapter 4 Discussions 91
Chapter 5 Conclusions 93
REFERENCES 95
PUBLICTION LIST 103
參考文獻 [1] Y.S. Yeh, K.N. Huang, S.L. Jen, Y.C. Li, M.S. Young, "Development of a Multitarget Tracking System for Paramecia", American Institute of Physics, Review of Scientific Instruments, 81(7), pp. 074302-8, 2010.
[2] N. Ogawa, H. Oku, K. Hashimoto, and M. Ishikawa, “Single-Cell Level Continuous Observation of Microorganism Galvanotaxis Using High-Speed Vision”, Proceedings of 2004 IEEE International Symposium on Biomedical Imaging (IEEE, Arlington, VA, USA, 2004), 2, pp.1331-1334, 2004.
[3] N. Ogawa, H. Oku, K. Hashimoto, and M. Ishikawa,“A Physical Model for Galvanotaxis of Paramecium Cell”, J Theor. Biol., 242(2), pp.314-328, 2006.
[4] I. Akitoshi and T. Hideki, “Control of Bioconvection and Its Mechanical Application”, Proceedings of 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (IEEE, Como, Italy, 2001), 2, pp.1220-1225, 2001.
[5] I. Akitoshi,“Motion Control of Protozoa for Bio-MEMS”, IEEE/ASME Trans. Mech., 5(2), pp.181-188, 2000.
[6] N. Ogawa, H. Oku, K. Hashimoto, and M. Ishikawa,” Microrobotic Visual Control of Motile Cells Using High-Speed Tracking System”, IEEE Trans. Robotics, 21(4), pp.704-712 ,2005.
[7] N. Ogawa, H. Oku, K. Hashimoto, and M. Ishikawa, “Motile Cell Galvanotaxis Control Using High-Speed Tracking System”, Proceedings of 2004 IEEE International Conference on Robotics and Automation (IEEE, New Orleans, LA, USA, 2004), 2, pp. 1646-1651, 2004.
[8] N. Ogawa, H. Oku, K. Hashimoto, and M. Ishikawa, “Dynamics Model of Paramecium Galvanotaxis for Microrobotic Application”, Proceedings of 2005 IEEE International Conference on Robotics and Automation (IEEE, Barcelona, Spain, 2005), pp.1246-1251, 2005.
[9] H. Machemer, “Electric Potentiation of Gravikinesis in Paramecium is Possibly Mediated by Filaments”, Adv. Space Res., 21(8), pp. 1301-1309, 1998.
[10] A. Hirano, T. Tsuji, N. Takiguchi, and H. Ohtake, “An Electrophysiological Model of Chemotactic Response in Paramecium “, Proceedings of 2006 IEEE International Conference on Man and Cybernetics (IEEE, Taipei, Taiwan, 2006), 5, pp. 3612-3617, 2006.
[11] T. Takahashi, M. Yoshii, T. Kawano, T. Kosaka, and H. Hosoya, “A New Approach for the Assessment of Acrylamide Toxicity Using A Green Paramecium”, Toxicology In Vitro, 19(1), pp.99-105 , 2005.
[12] K. Kawamoto, Y. Nishikawa, K. Oami, Y. Jin, I. Sato, N. Saito, and S. Tsuda, “Effects of Perfluorooctane sulfonate (PFOS) on Swimming Behavior and Membrane Potential of Paramecium Caudatum”, J Toxicol. Sci., 33, pp.155-161 , 2008.
[13] J. Bernal, S. Ruvalcaba, “Pharmacological Prevention of Acute Lead Poisoning in Paramecium”, Toxicology, vol.108, no.3, pp.165-173, 1996.
[14] P. Madoni, “The Acute Toxicity of Nickel to Freshwater Ciliates”, Environmental Pollution, 109(1), pp.53-59, 2000.
[15] J. Venkateswara Rao, K. Srikanth, S.K. Arepalli, and V.G. Gunda, “Toxic Effects of Acephate on Paramecium Caudatum with Special Emphasis on Morphology, Behaviour, and Generation Time”, Pestic. Biochem. Physiol., 86, pp.131-137, 2006.
[16] J. Venkateswara Rao, V. G. Gunda, K. Srikanth, and S. K. Arepalli, “Acute Toxicity Bioassay Using Paramecium Caudatum, A Key Member to Study the Effects of Monocrotophos on Swimming Behaviour, Morphology and Reproduction”, Toxicol. Environ. Chem., 89(2), pp.307-317, 2007.
[17] J. Venkateswara Rao, S.K. Arepalli, V.G. Gunda, and J. Bharat Kumar, “Assessment of Cytoskeletal Damage in Paramecium Caudatum: An Early Warning System for Apoptotic Studies”, Pesticide Biochemistry and Physiology, 91(2), pp.75-80, 2008.
[18] H. Oku, N. Ogawa, M. Ishikawa, and K. Hashimoto, “Two-dimensional Tracking of a Motile Micro-Organism Allowing High-Resolution Observation with Various Imaging Techniques", Rev. Sci. Instrum., 76(3), pp.034301-1-9, 2005.
[19] X. Fei, Y. Igarashi, and K. Hashimoto, ” 2D Tracking of Single Paramecium by Using Parallel Level Set Method and Visual Servoing”, Proceedings of 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (IEEE, Xi′ an, China, 2008), pp.752-757, 2008.
[20] H. Oku, I. Ishii, and M. Ishikawa, “Tracking a Protozoon Using High-Speed Visual Feedback”, Proceedings of 2000 1st Annual International Conference On Microtechnologies in Medicine and Biology (IEEE, Lyon, France, 2000), pp.156-159, 2000.
[21] B. Taboada, S. Poggio, L. Camarena, and G. Corkidi, “Automatic Tracking and Analysis System for Free-Swimming Bacteria”, Proceedings of 2003 the 25th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (IEEE, Albuquerque, New Mexico, USA, 2003 ), 1 ,pp.906-909, 2003.
[22] Y. J. Chen, Y. C. Li, K. N. Huang, S. L. Jen, M. S. Young, “Video tracking algorithm of long-term experiment using stand-alone recording system”, Rev. Sci. Instrum., 79(8), pp. 085108-1-8, 2008.
[23] Y. J. Chen, Y. C. Li, K. N. Huang, S. L. Jen, M. S. Young, “Stand-Alone Video-Based Animal Tracking System for Noiseless Application“, Instrumentation Science and Technology, 37(3), pp. 366-378, 2009.
[24] A. O. Koob, J. Cirillo, and C. F. Babbs, “A Novel Open Field Activity Detector to Determine Spatial and Temporal Movement of Laboratory Animals After Injury and Disease”, J. Neurosci. Methods 157(2), pp.330-336, 2006.
[25] M. Grossmann and M. H. Skinner, “A Simple Computer Based System to Analyze Morris Water Maze Trials On-Line”, J. Neurosci. Methods, 70(2), pp.171-175, 1996.
[26] P. D. Martin, H. Nishijo, and T. Ono, “A Combined Electrophysiological and Video Data Acquisition System Using a Single Computer “, J. Neurosci. Methods, 92(1-2), pp.169-177, 1999.
[27] L. Noldus, A. J. Spink, and R. A. J. Tegelenbosch, “EthoVision: a versatile video tracking system for automation of behavioral experiments”, Behav. Res. Methods Instrum. Comput., 33(3), pp. 398-414, 2001.
[28] A. J. Spink, R. A. J. Tegelenbosch, M. O. S. Buma, and L. Noldus, “The EthoVision Video Tracking System - A Tool for Behavioral Phenotyping of Transgenic Mice “, Physiol. Behav., 73(5), pp. 731-744, 2001.
[29] D. P. Wolfer, R. Madani, P. Valenti, and H. P. Lipp, “Extended Analysis of Path Data From Mutant Mice Using The Public Domain Software Wintrack “, Physiol. Behav. , 73(5), pp.745-753, 2001.
[30] B. M. Wu, F. H. Y. Chan, F. K. Lam, P. W. F. Poon, and A. M. S. Poon, “A Novel System for Simultaneous Monitoring of Locomotor and Sound Activities in Animals “, J. Neurosci. Methods , 101(1), pp. 69-73, 2000.
[31] Y. H. Shih and M. S. Young, “Integrated Digital Image and Accelerometer Measurements of Rats Locomotor and Vibratory Behavior”, J. Neurosci. Methods, 166(1), pp.81-88, 2007.
[32] A. B. L. Tort, W. P. Neto, O. B. Amaral, V. Kazlauckas, D. O. Souza, D. R. Lara, “A Simple Webcam-Based Approach for The Measurement of Rodent Locomotion and Other Behavioural Parameters”, J. Neurosci. Methods , 157(1), pp.91-97, 2006.
[33] D. Ramanan, D.A Forsyth, K. Barnard, “Building Models of Animals from Video”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(8), pp. 1319-1334, 2006.
[34] A. Kaska, H. P. Nguyenb, R. Pabstb and S. von Horstenb, “Factors Influencing Behavior of Group-Housed Male Rats in The Social Interaction Test Focus on Cohort Emoval”, Physiology & Behavior, 74(3), pp. 277-282 , 2001.
[35] W. Niblack, An Introduction to Digital Image Processing, pp. 115-116, Prentice Hall, 1986.
[36] S. Mukhopadhyay, B. Chanda, “An Edge Preserving Noise Smoothing Technique Using Multiscale Morphology”, Signal Processing, 82(4), pp.527-544, 2002.
[37] R. M. Haralick, S. R. Sternberg, X. Zhuang, “Image Analysis Using Mathematical Morphology”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4), pp. 532-550, 1987.
[38] B.M. Mehtre,” Fingerprint image analysis for automatic identification” , Machine Vision and Applications, 6(2), pp.124-139, 1993.
[39] A. Almansa, T. Lindeberg, “Fingerprint Enhancement by Shape Adaptation of Scale-Space Operators with Automatic Scale-Selection“, IEEE Transactions on Image Processing, 9(12), pp.2027-2042, 2000.
[40] A Toprak, İ Güler, “Impulse Noise Reduction in Medical Images with The Use of Switch Mode Fuzzy Adaptive Median Filter”, Digital Signal Processing, 17(4), pp.711-723, 2007.
[41] J. Park, A. Tabb, and A.C Kak,“Hierarchical Data Structure for Real-Time Background Subtraction”, Proceedings of 2006 IEEE International Conference on Image Processing (IEEE, Atlanta, GA, 2006), pp. 1849-1852, 2006.
[42] E. Zhang, F. Chen, and W. Zhang, “A Novel Particle Filter Based Background Subtraction Method”, Proceedings of 2006 International Conference on Computational Intelligence and Security (IEEE, Beijing, China, 2008), vol.2, pp. 1837-1840, 2008.
[43] L. Jia, Y. Liu,“A Novel Thresholding Approach to Background Subtraction”, Proceedings of 2008 IEEE Workshop on Applications of Computer Vision (IEEE, Colorado, USA, 2008), pp.1-6, 2008.
[44] M Stanojevic, S. Vranes, and D. Velasevic,“Pattern matching in search problem solving”, Proceedings of 1996 the Twenty-Ninth Hawaii International Conference on System Sciences (IEEE, Wailea, HI, USA, 2008), 2, pp.201-209, 2008.
[45] P. Ariano, C. Distasi, A. Gilardino, P. Zamburlin, M. Ferraro, “A Simple Method to Study Cellular Migration”, J. Neurosci. Methods, 141(2), pp.271-276, 2004.
[46] N. Otsu, “A Tlreshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systems, Man, and Cybernetics, 9, 62-66, 1979.
[47] W. H. Walton, “Feret‘s Statistical Diameter as a Measure of Particle Size”, Nature, 162, pp.329-330, 1948.
[48] J. Tóvári, R. Gilly, E. Rásó, S. Paku, B. Bereczky, N. Varga, Á. Vágó and J. Tímár, “Recombinant human erythropoietin alpha targets intratumoral blood vessels, improving chemotherapy in human xenograft models“, Cell and Tumor Biology, 65(16), pp.7186-7193, 2005.
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