Author: |
張哲榮 Chang, Che-Jung |
---|---|
Thesis Title: |
基於影像處理之斑馬魚運動軌跡分析 Motion Trajectory Analysis of Zebrafish Based on Image Processing |
Advisor: |
賀耀華
Ho, Yao-Hua 林豊益 Lin, Li-Yih |
Committee: | 周銘翊 陳伶志 方瓊瑤 賀耀華 林豊益 |
Approval Date: | 2021/10/12 |
Degree: |
碩士 Master |
Department: |
資訊工程學系 Department of Computer Science and Information Engineering |
Thesis Publication Year: | 2021 |
Academic Year: | 109 |
Language: | 中文 |
Number of pages: | 49 |
Keywords (in Chinese): | 斑馬魚行為 、影像處理 、MEI 、MHI |
Keywords (in English): | Image Processing, Motion Object Tracking, Zebrafish |
Research Methods: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202101716 |
Thesis Type: | Academic thesis/ dissertation |
Reference times: | Clicks: 200 Downloads: 28 |
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在過去的研究中,發現斑馬魚的基因與人類相近,其基因序列中可找到約八成與人類疾病相關的基因,且斑馬魚可以迅速繁殖,相較於過往實驗用的白老鼠所花費成本更低 。 在神經行為學的研究上科學家透過斑馬魚的行為,了解斑馬魚中樞神經系統的運作 不過由於斑馬魚個體間外型相近,在進行實驗時 科學家不易辨識分析 追蹤個體 且在 過去 研究上 都是利用肉眼觀看實驗過程 ,或是透過觀看錄影影片觀察斑馬魚行為,此過程須花費不少人力和時間成本,且使用者無法同時進行觀看多種斑馬魚實驗。
本研究利用影像動態追蹤的技術,運用運動歷史圖 ( Motion History Image, MHI ) 演算法提出一套斑馬魚影像分析系統 Fish Motion Tracking (FMT) System 且提出一個提高追蹤精準度的方法-方向向量推算法,此方法將用於斑馬魚影像重疊後,如何辨別追蹤斑馬魚個體為何 FMT 系統會將斑馬魚的影像數據轉換成實驗數據 (斑馬魚座標和游動方向向量 科學家可以利用這些數據分析斑馬魚游動與行為,進而減少過去研究中所需花費的人力和時間成本。
According to the previously conducted research, Zebrafish is a popular organism and widespread use for the study of vertebrate gene function and human genetic disease due to the similarity to human. In fact, the gene sequence of Zebrafish has approximately 80% related to human diseases. Therefore, Zebrafish are more useful than Albino mice to research experiments. In the Neuroethology of Zebrafish’s research, scientists use Zebrafish behavior to understand the operation of the Zebrafish’s Central Nervous System. However, due to the similar appearance of Zebrafish individuals, it is difficult for scientists to identify, analyze, and track individuals during experiments. In the past, the conducted experiments on the behavior of Zebrafish often use naked eyes or videotape to monitor the results. Therefore, a lot of labor and time are required to conduct multiple experiments. Also, it is difficult to analyze individual Zebrafish’s behavior simultaneously due to similar body shapes. In this research, we proposed the Fish Motion Tracking (FMT) system to dynamically object tracking a set of Zebrafish. By taking advantage of the image process approach, we are able to improve the accuracy of the tracking result with the Motion History Image (MHI) algorithm and Direction Vector Inference (DVI) algorithm. The contributions of the proposed FMT system are as follow: 1) it improves the accuracy of the tracking results of Zebrafish; 2) the system is able to identify the individual Zebrafish after multiple Zebrafish overlap each other; and 3) the FMT system converts the Zebrafish image data into experimental data (Zebrafish coordinates and swimming direction vectors) which scientists can use these data to analyze Zebrafish behavior by the individual of the Zebrafish data. The proposed FMT system can greatly reduce the labor and time costs required in the related research.
[1] K. Dooley and L. I. Zon, "Zebrafish: a model system for the study of human disease," Current Opinion in Genetics & Development, vol. 10, no. 3, pp. 252-256, 2000/06/01/ 2000, doi: https://doi.org/10.1016/S0959-437X(00)00074-5.
[2] M. B. Orger and G. G. d. Polavieja, "Zebrafish Behavior: Opportunities and Challenges," Annual Review of Neuroscience, vol. 40, no. 1, pp. 125-147, 2017, doi: 10.1146/annurev-neuro-071714-033857.
[3] J. Cachat et al., "Three-Dimensional Neurophenotyping of Adult Zebrafish Behavior," PLOS ONE, vol. 6, no. 3, p. e17597, 2011, doi: 10.1371/journal.pone.0017597.
[4] C.-W. Fu et al., "Exposure to silver impairs learning and social behaviors in adult zebrafish," Journal of Hazardous Materials, vol. 403, p. 124031, 2021/02/05/ 2021, doi: https://doi.org/10.1016/j.jhazmat.2020.124031.
[5] C. Buske and R. Gerlai, "Maturation of shoaling behavior is accompanied by changes in the dopaminergic and serotoninergic systems in zebrafish," Developmental Psychobiology, https://doi.org/10.1002/dev.20571 vol. 54, no. 1, pp. 28-35, 2012/01/01 2012, doi: https://doi.org/10.1002/dev.20571.
[6] M. Agetsuma et al., "The habenula is crucial for experience-dependent modification of fear responses in zebrafish," Nature Neuroscience, vol. 13, no. 11, pp. 1354-1356, 2010/11/01 2010, doi: 10.1038/nn.2654.
[7] J. E. Franco-Restrepo, D. A. Forero, and R. A. Vargas, "A Review of Freely Available, Open-Source Software for the Automated Analysis of the Behavior of Adult Zebrafish," Zebrafish, vol. 16, no. 3, pp. 223-232, 2019/06/01 2019, doi: 10.1089/zeb.2018.1662.
[8] A. Pérez-Escudero, J. Vicente-Page, R. C. Hinz, S. Arganda, and G. G. de Polavieja, "idTracker: tracking individuals in a group by automatic identification of unmarked animals," Nature Methods, vol. 11, no. 7, pp. 743-748, 2014/07/01 2014, doi: 10.1038/nmeth.2994.
[9] V. H. Sridhar, D. G. Roche, and S. Gingins, "Tracktor: Image-based automated tracking of animal movement and behaviour," Methods in Ecology and Evolution, vol. 10, no. 6, pp. 815-820, 2019, doi: https://doi.org/10.1111/2041-210X.13166.
[10] J. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, vol. 1, no. 14: Oakland, CA, USA, pp. 281-297.
[11] D. Bradley and G. Roth, "Adaptive Thresholding using the Integral Image," Journal of Graphics Tools, vol. 12, no. 2, pp. 13-21, 2007/01/01 2007, doi: 10.1080/2151237X.2007.10129236.
[12] H. Liu and K. C. Jezek, "Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods," International Journal of Remote Sensing, vol. 25, no. 5, pp. 937-958, 2004/03/01 2004, doi: 10.1080/0143116031000139890.
[13] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979, doi: 10.1109/TSMC.1979.4310076.
[14] Z. Xu and X. E. Cheng, "Zebrafish tracking using convolutional neural networks," Scientific Reports, vol. 7, no. 1, p. 42815, 2017/02/17 2017, doi: 10.1038/srep42815.
[15] R. M. Haralick, S. R. Sternberg, and X. Zhuang, "Image Analysis Using Mathematical Morphology," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 4, pp. 532-550, 1987, doi: 10.1109/TPAMI.1987.4767941.
[16] S. Kato et al., "A computer image processing system for quantification of zebrafish behavior," Journal of Neuroscience Methods, vol. 134, no. 1, pp. 1-7, 2004/03/15/ 2004, doi: https://doi.org/10.1016/j.jneumeth.2003.09.028.
[17] A. Bobick and J. Davis, "An appearance-based representation of action," in Proceedings of 13th International Conference on Pattern Recognition, 25-29 Aug. 1996 1996, vol. 1, pp. 307-312 vol.1, doi: 10.1109/ICPR.1996.546039.
[18] J. W. Davis and A. F. Bobick, "The representation and recognition of human movement using temporal templates," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 17-19 June 1997 1997, pp. 928-934, doi: 10.1109/CVPR.1997.609439.
[19] A. F. Bobick and J. W. Davis, "The recognition of human movement using temporal templates," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 257-267, 2001, doi: 10.1109/34.910878.
[20] J. W. Davis, "Hierarchical motion history images for recognizing human motion," in Proceedings IEEE Workshop on Detection and Recognition of Events in Video, 8-8 July 2001 2001, pp. 39-46, doi: 10.1109/EVENT.2001.938864.
[21] Y. Zhaozheng and R. Collins, "Moving Object Localization in Thermal Imagery by Forward-backward MHI," in 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 17-22 June 2006 2006, pp. 133-133, doi: 10.1109/CVPRW.2006.131.
[22] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, "Fall Detection from Human Shape and Motion History Using Video Surveillance," in 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 21-23 May 2007 2007, vol. 2, pp. 875-880, doi: 10.1109/AINAW.2007.181.
[23] A. Buades, B. Coll, and J. M. Morel, "A Review of Image Denoising Algorithms, with a New One," Multiscale Modeling & Simulation, vol. 4, no. 2, pp. 490-530, 2005, doi: 10.1137/040616024.
[24] P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, "Contour Detection and Hierarchical Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, 2011, doi: 10.1109/TPAMI.2010.161.
[25] S. Huang, F. Cheng, and Y. Chiu, "Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution," IEEE Transactions on Image Processing, vol. 22, no. 3, pp. 1032-1041, 2013, doi: 10.1109/TIP.2012.2226047.
[26] F. Drago, K. Myszkowski, T. Annen, and N. Chiba, "Adaptive Logarithmic Mapping For Displaying High Contrast Scenes," Computer Graphics Forum, https://doi.org/10.1111/1467-8659.00689 vol. 22, no. 3, pp. 419-426, 2003/09/01 2003, doi: https://doi.org/10.1111/1467-8659.00689.
[27] H. W. Kuhn, "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, vol. 2, no. 1-2, pp. 83-97, 1955, doi: https://doi.org/10.1002/nav.3800020109.
[28] E. Hamuda, B. Mc Ginley, M. Glavin, and E. Jones, "Improved image processing-based crop detection using Kalman filtering and the Hungarian algorithm," Computers and electronics in agriculture, vol. 148, pp. 37-44, 2018.