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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張恆華(Herng-Hua Chang) | |
| dc.contributor.author | Yi-Ru Lin | en |
| dc.contributor.author | 林逸儒 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:18:10Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:18:10Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-04 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80829 | - |
| dc.description.abstract | "腦中風是造成全球人口死亡與失能的主要原因之一，近年來有許多腦中風之相關研究，在臨床實驗模型中，大多使用囓齒類動物之影像作為實驗研究依據。為了將中風區域分割出來，不僅需要專家耗時且費力的進行手動分割，也容易因為各人的評判標準不同而產生不一致之結果。因此本篇論文以大鼠作為實驗動物，利用其腦部磁振影像與經2,3,5-氯化三苯基四氮唑染色之大腦影像開發一自動演算法，將中風區域分割，以利研究者進行分析與研究。本篇論文主要以大腦動脈阻塞之大鼠作為研究對象，針對缺血性中風之鼠腦進行中風區分割。演算法分為以下幾個部分，首先因為缺血性中風位置之像素影像強度較高，利用左右腦之影像進行影像套合後之差值判定為中風區域初始輪廓，接著套用改良的可變形模型得到分割後的中風區影像。改良的可變形模型是基於窄帶區域的無邊緣主動輪廓模型，並將窄帶區域改成更局部的法線方向，並在迭代過程中對時間步長進行調整，使計算效率提升、準確率上升。本篇研究使用擴散權重磁振影像共有67隻大鼠， T2權重影像共有76隻大鼠， 2，3，5－氯化三苯基四氮唑染色影像共有43隻大鼠。結果顯示在上述三種不同鼠腦影像中，本研究演算法可以產生優良的大鼠大腦中風區分割結果。在相同的實驗設定下，本論文提出之方法優於其他可變形模型之分割結果。本研究提出一個全自動的鼠腦中風區分割方法，可以成為良好的輔佐工具，協助進行腦中風相關之研究。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:18:10Z (GMT). No. of bitstreams: 1 U0001-0110202114315100.pdf: 4765110 bytes, checksum: 2ad8c9e13e8ca3b5ba502622fde59131 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 i 中文摘要 ii Abstract iii 目錄 v 圖目錄 viii 表目錄 xi 第 1 章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 2 1.4 論文架構 3 第 2 章 文獻探討 4 2.1 磁振造影 4 2.2 2，3，5－氯化三苯基四氮唑染色鼠腦影像 5 2.3 消除磁振影像雜訊之濾波器 6 2.3.1 平均濾波器 7 2.3.2 中值濾波器 8 2.3.3 Alpha-修整平均濾波器 8 2.3.4 高斯濾波器 9 2.4 影像套合 10 2.4.1 B樣條影像套合 10 2.4.2 巴氏距離 12 2.4.3 貪婪演算法影像套合 12 2.4.4 惡魔演算法影像套合 13 2.5 影像增強 14 2.5.1 伽瑪校正 14 2.5.2 局部伽瑪校正 15 2.6 影像分割演算法 15 2.6.1 形態學 15 2.6.2 強度 16 2.6.3 可變形模型 17 2.7 色彩空間模型 21 2.7.1 RGB色彩空間 21 2.7.2 CIELAB色彩空間 21 2.8 鼠腦中風區域分割演算法 23 2.8.1 鼠腦磁振影像中風區域分割 23 2.8.2 鼠腦TTC影像中風區域分割 24 第 3 章 研究方法設計 25 3.1 資料集 25 3.2 磁振影像中風區分割 25 3.2.1 影像預處理 27 3.2.2 影像套合 27 3.2.3 改進的可變形模型 30 3.2.4 利用改進模型進行分割 34 3.3 TTC影像中風區分割 35 3.3.1 影像預處理 36 3.3.2 中風區域初始輪廓 36 3.3.3 胼胝體去除 36 3.3.4 中風區域初始輪廓閥值計算 39 3.3.5 利用改進模型進行分割 39 第 4 章 實驗結果與討論 41 4.1 實驗說明 41 4.1.1 實驗環境 41 4.1.2 資料集 41 4.1.3 評估標準 41 4.2 參數設定 44 4.2.1 擴散權重磁振影像 44 4.2.2 T2權重影像 47 4.2.3 TTC影像 50 4.3 中風區分割結果 54 4.3.1 擴散權重磁振影像大腦中風區分割結果 54 4.3.2 T2權重影像大腦中風區分割結果 66 4.3.3 TTC影像大腦中風區分割結果 80 第 5 章 結論與未來展望 92 5.1 結論 92 5.2 未來展望 92 參考文獻 94 | |
| dc.language.iso | zh-TW | |
| dc.subject | 水平集方法 | zh_TW |
| dc.subject | 缺血型中風 | zh_TW |
| dc.subject | 2，3，5－氯化三苯基四氮唑染色影像 | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 影像套合 | zh_TW |
| dc.subject | 磁振影像 | zh_TW |
| dc.subject | 動態輪廓模型 | zh_TW |
| dc.subject | Magnetic resonance imaging (MRI) | en |
| dc.subject | 5-triphenyl tetrazolium chloride (TTC) | en |
| dc.subject | ischemic stroke | en |
| dc.subject | image segmentation | en |
| dc.subject | image registration | en |
| dc.subject | deformable model | en |
| dc.subject | level set method | en |
| dc.title | 基於水平集可變形模型之鼠腦影像中風區域分割之研究 | zh_TW |
| dc.title | Infarct Region Segmentation in Rat Brain Images After Stroke Using Level Set-Based Deformable Models | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 江明彰(Hsin-Tsai Liu),張瑞益(Chih-Yang Tseng),葉馨喬 | |
| dc.subject.keyword | 磁振影像,2，3，5－氯化三苯基四氮唑染色影像,缺血型中風,影像分割,影像套合,動態輪廓模型,水平集方法, | zh_TW |
| dc.subject.keyword | Magnetic resonance imaging (MRI),2,3,5-triphenyl tetrazolium chloride (TTC),ischemic stroke,image segmentation,image registration,deformable model,level set method, | en |
| dc.relation.page | 98 | |
| dc.identifier.doi | 10.6342/NTU202103498 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-05 | |
| 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 |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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| U0001-0110202114315100.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 4.65 MB | Adobe PDF |
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