Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73987Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 張恆華 | |
| dc.contributor.author | Wei-You Lin | en |
| dc.contributor.author | 林瑋祐 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:15:54Z | - |
| dc.date.available | 2019-08-20 | |
| dc.date.copyright | 2019-08-20 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-14 | |
| dc.identifier.citation | [1] The top 10 causes of death. [Online]. Available: https://www.who.int/topics/cerebrovascular_accident/en/
[2] J. Bamford, P. Sandercock, L. Jones, and C. Warlow, 'The natural history of lacunar infarction: the Oxfordshire Community Stroke Project,' Stroke, vol. 18, no. 3, pp. 545-551, 1987. [3] J. F. Schenck, 'The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds,' Medical physics, vol. 23, no. 6, pp. 815-850, 1996. [4] J. Kandampully and D. Suhartanto, 'Customer loyalty in the hotel industry: the role of customer satisfaction and image,' International journal of contemporary hospitality management, vol. 12, no. 6, pp. 346-351, 2000. [5] J. B. Bederson, L. H. Pitts, S. Germano, M. Nishimura, R. Davis, and H. Bartkowski, 'Evaluation of 2, 3, 5-triphenyltetrazolium chloride as a stain for detection and quantification of experimental cerebral infarction in rats,' Stroke, vol. 17, no. 6, pp. 1304-1308, 1986. [6] K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, 'Automated model-based tissue classification of MR images of the brain,' IEEE transactions on medical imaging, vol. 18, no. 10, pp. 897-908, 1999. [7] E. J. Goldlust, R. P. Paczynski, Y. Y. He, C. Y. Hsu, and M. P. Goldberg, 'Automated measurement of infarct size with scanned images of triphenyltetrazolium chloride-stained rat brains,' Stroke, vol. 27, no. 9, pp. 1657-1662, 1996. [8] P. H. Pevsner et al., 'A photothrombotic model of small early ischemic infarcts in the rat brain with histologic and MRI correlation,' Journal of pharmacological and toxicological methods, vol. 45, no. 3, pp. 227-233, 2001. [9] L. E. Towill and P. Mazur, 'Studies on the reduction of 2, 3, 5-triphenyltetrazolium chloride as a viability assay for plant tissue cultures,' Canadian Journal of Botany, vol. 53, no. 11, pp. 1097-1102, 1975. [10] 常淑芳 and 美禮, '氯化三苯基四氮唑染色在高度超生物焦域中的…,' 中超志, vol. 16, no. 9, pp. 641-644, 2000. [11] Y. H. Wang and T. L. Fu., 'Research on segmentation methods of brain using mri images.,' 2011 International Conference on Energy and Environmental Science, no. Energy Procedia, pp. 2382--2388, 2011. [12] R. Stokking, K. L. Vincken, and M. A. Viergever, 'Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data,' NeuroImage, vol. 12, no. 6, pp. 726-738, 2000. [13] D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, 'Magnetic resonance image tissue classification using a partial volume model,' NeuroImage, vol. 13, no. 5, pp. 856-876, 2001. [14] C. M. Bishop, Pattern recognition and machine learning. springer, ' Canadian Journal of Botany, vol. 54, no. 11, pp. 500-546, 2006. [15] T. F. Chan and L. A. Vese, 'Active contours without edges,' IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266-277, 2001. [16] P. Bao, L. Zhang, and X. Wu, 'Canny edge detection enhancement by scale multiplication,' IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 9, pp. 1485-1490, 2005. [17] A. Neubeck and L. Van Gool, 'Efficient non-maximum suppression,' in 18th International Conference on Pattern Recognition, vol. 2, no.4,pp. 850-855,2006. [18] P. Getreuer, 'Chan-vese segmentation,' Image Processing On Line, vol. 2, pp. 214-224, 2012. [19] J. Yang, C. Liu, and L. Zhang, 'Color space normalization: Enhancing the discriminating power of color spaces for face recognition,' Pattern Recognition, vol. 43, no. 4, pp. 1454-1466, 2010. [20] G. Swain and S. K. Lenka, 'A Novel Approach to RGB Channel Based Image Steganography Technique,' Int. Arab J. e-Technol., vol. 2, no. 4, pp. 181-186, 2012. [21] A. Koschan and M. Abidi, Digital color image processing. John Wiley & Sons, Journal of pharmacological and toxicological methods, vol. 45, no. 3, pp. 162-201, 2008. [22] G. M. Johnson and M. D. Fairchild, 'A top down description of S‐CIELAB and CIEDE2000,' Color Research & Application: Endorsed by Inter‐Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur, vol. 28, no. 6, pp. 425-435, 2003. [23] S.-C. Huang, F.-C. Cheng, and Y.-S. Chiu, 'Efficient contrast enhancement using adaptive gamma correction with weighting distribution,' IEEE transactions on image processing, vol. 22, no. 3, pp. 1032-1041, 2012. [24] H. Farid, 'Blind inverse gamma correction,' IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1428-1433, 2001. [25] L. Itti, C. Koch, and E. Niebur, 'A model of saliency-based visual attention for rapid scene analysis,' IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 11, pp. 1254-1259, 1998. [26] L. Itti and C. Koch, 'Computational modelling of visual attention,' Nature reviews neuroscience, vol. 2, no. 3, p. 194, 2001. [27] M. Agrawal, K. Konolige, and M. R. Blas, 'Censure: Center surround extremas for realtime feature detection and matching,' in European Conference on Computer Vision: Springer, pp. 102-115, 2008. [28] R. M. Klein, 'Inhibition of return,' Trends in cognitive sciences, vol. 4, no. 4, pp. 138-147, 2000. [29] C. E. Rusbult, J. M. Martz, and C. R. Agnew, 'The investment model scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size,' Personal relationships, vol. 5, no. 4, pp. 357-387, 1998. [30] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, 'Slic superpixels,' IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 9, pp. 595-800, 2010. [31] A. K. Jain, 'Data clustering: 50 years beyond K-means,' Pattern recognition letters, vol. 31, no. 8, pp. 651-666, 2010. [32] M.-M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.-M. Hu, 'Global contrast based salient region detection,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569-582, 2014. [33] W. Gao, X. Zhang, L. Yang, and H. Liu, 'An improved Sobel edge detection,' in 2010 3rd International Conference on Computer Science and Information Technology, vol. 5: IEEE, pp. 67-71, 2010. [34] D. V. Widder, Laplace transform (PMS-6). Princeton university press, NeuroImage, vol. 13, no. 5, pp. 856-876, 2015. [35] L. Nan and P. Wonka, 'Polyfit: Polygonal surface reconstruction from point clouds,' in Proceedings of the IEEE International Conference on Computer Vision, ,pp. 2353-2361, 2017. [36] J. C. Lee et al., 'Accelerated cerebral ischemic injury by activated macrophages/microglia after lipopolysaccharide microinjection into rat corpus callosum,' Glia, vol. 50, no. 2, pp. 168-181, 2005. [37] H.-H. Chang, A. H. Zhuang, D. J. Valentino, and W.-C. Chu, 'Performance measure characterization for evaluating neuroimage segmentation algorithms,' Neuroimage, vol. 47, no. 1, pp. 122-135, 2009. [38] L. R. Dice, 'Measures of the amount of ecologic association between species,' Ecology, vol. 26, no. 3, pp. 297-302, 1945. [39] P. Jaccard, 'THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE,' New Phytologist, Article vol. 11, no. 2, pp. 37-50, 1912 [40] O. Ronneberger, P. Fischer, and T. Brox, 'U-Net: Convolutional Networks for Biomedical Image Segmentation,' Journal of pharmacological and toxicological methods, vol. 45, no. 3, pp. 227-233, 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73987 | - |
| dc.description.abstract | 近年來的研究顯示，中風為造成國人死亡的主要原因之一。而腦中風為腦血管阻塞或自發性破裂而造成腦部功能失常的疾病，分為缺血性及出血性，造成腦部缺乏養分及氧氣而導致腦損傷或死亡。因此在臨床研究中，腦中風為關注的議題，在臨床實驗模型中，大多使用齧齒動物之實驗影像作為研究依據。醫生須手動圈選梗塞部分，相當耗時，因此本研究提出一套演算法自動分割鼠腦梗塞區域。
本研究主要以缺血性中風鼠腦為研究對象，針對大腦動脈阻塞的大鼠為實驗對象，並以經2，3，5－氯化三苯基四氮唑染色的原始大腦影像為研究影像。本演算法首先透過顯著區域之偵測進行背景去除，之後進行伽瑪校正，得到乾淨且明亮的鼠腦影像；再進行大鼠腦部中線偵測，並且透過CIELab顏色辨別加入可變形模型進行中風區域分割。 本研究使用47隻大鼠，總共切割出348張大鼠腦部中風影像。實驗結果顯示，本研究之方法能有效的切割出鼠腦中風區域，Dice值84.8， Jaccard值為75.9， Conformity值為69.6比其他方法優秀，與醫師所切割出之結果相近。 | zh_TW |
| dc.description.abstract | According to recent research, stroke is one of the causes of death nationwide. Stroke is a disease of brain dysfunction caused by cerebral vascular occlusion or spontaneous rupture, which can be divided into ischemic and hemorrhagic. Both types of stroke lead to nutrient and oxygen deprivation in the brain, which ultimately cause brain damage or death. Therefore, stroke is a topic of clinical concern. Usually different types of imaging modalities of rodents are used as the research basis. It is quite time consuming for researchers to manually circle the infarct. This thesis proposes an algorithm to automatically segment the rat cerebral infarction area.
This study focuses on ischemic stroke by using rat with cerebral artery occlusion as experimental subjects and examines the images of their brains obtained from staining with 2,3,5-triphenyl tetrazolium chloride (TTC). Firstly, the algorithm removes image background via the salient region detection method. After removing the background, we perform gamma correction and obtain the clean and bright brain image, after that, we can do the brain center line detection, and the segmentation of the stroke region is based on a deformable model via a CIELab distinguishing strategy. In this study, 47 rats were used to produce a total of 348 rat brain stroke images. The experimental results showed that this study can effectively segment the rat brain stroke images, and the Dice = 84.8, Jaccard = 75.9, Conformity = 69.6 is better than other methods and closer to the estimates by doctors. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:15:54Z (GMT). No. of bitstreams: 1 ntu-108-R05525127-1.pdf: 9320298 bytes, checksum: fe0e11384cfdd9371784f46d72f9663c (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii 目錄 iv 圖目錄 vii 表目錄 x 第 1 章 緒論 1 1.1 研究背景 1 1.2 研究目的 4 1.3 論文大綱 4 第 2 章 文獻回顧 5 2.1 2，3，5－氯化三苯基四氮唑染色的大腦影像(2,3,5-triphenyl tetrazolium chloride (TTC)) 5 2.2 腦部分割演算法 6 2.2.1 侵蝕與膨脹 6 2.2.2 Canny邊緣檢測器 7 2.2.3 無邊緣主動輪廓模型 9 2.3 色彩空間模型 12 2.3.1 RGB色彩空間 12 2.3.2 CIELAB色彩空間 13 2.4 伽瑪校正演算法 15 2.5 圖像顯著性 16 2.5.1 視覺顯著性檢測 17 2.5.2 認知注意模型 17 2.5.3 決策論注意模型 18 2.5.4 頻域分析注意模型 18 2.5.5 圖論分析注意模型 19 第 3 章 研究設計與方法 20 3.1 資料集 20 3.2 方法與流程 20 3.2.1 鼠腦真實影像擷取 20 3.2.1.1 顯著區域檢測 21 3.2.2 左右半腦之區域分割 24 3.2.2.1 多項式回歸 28 3.2.3 缺血性中風區域之分割 29 3.3 系統架構流程圖 (Flow chart) 32 第 4 章 實驗結果與討論 33 4.1 資料集 33 4.2 評估標準 33 4.3 真實鼠腦TTC影像 36 4.3.1 顯著性區域檢測方法評估 36 4.3.2 其他方法比較 42 4.4 左右腦分割TTC影像 44 4.4.1 Canny邊緣偵測與形態學分割左右腦之評估 44 4.5 缺血型中風區域TTC影像 51 4.5.1 可變形模型分割缺血性中風區域之評估 51 4.5.2 其他方法比較 58 第 5 章 結論與未來展望 59 5.1 結論 59 5.2 未來展望 60 參考文獻 61 | |
| dc.language.iso | zh-TW | |
| dc.subject | 中線偵測 | zh_TW |
| dc.subject | 2，3，5－氯化三苯基四氮唑 | zh_TW |
| dc.subject | 缺血性中風 | zh_TW |
| dc.subject | 顯著區域偵測 | zh_TW |
| dc.subject | 中風區域分割 | zh_TW |
| dc.subject | 伽瑪校正 | zh_TW |
| dc.subject | gamma correction | en |
| dc.subject | ischemic stroke | en |
| dc.subject | 5-triphenyl tetrazolium chloride (TTC) | en |
| dc.subject | stroke region segmentation | en |
| dc.subject | center line detection | en |
| dc.subject | salient region detection | en |
| dc.title | 利用顯著區域檢測與可變形模型從事鼠腦中風梗塞影像之研究 | zh_TW |
| dc.title | Using Salient Region Detection and Deformable Models to Analyze Infarct Regions in Rat Brain Images | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張瑞益,江明彰,葉馨喬 | |
| dc.subject.keyword | 缺血性中風,2，3，5－氯化三苯基四氮唑,顯著區域偵測,伽瑪校正,中線偵測,中風區域分割, | zh_TW |
| dc.subject.keyword | ischemic stroke,2,3,5-triphenyl tetrazolium chloride (TTC),salient region detection,gamma correction,center line detection,stroke region segmentation, | en |
| dc.relation.page | 64 | |
| dc.identifier.doi | 10.6342/NTU201902948 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-15 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| Appears in Collections: | 工程科學及海洋工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-108-1.pdf Restricted Access | 9.1 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.