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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張恆華(Herng-Hua Chang) | |
| dc.contributor.author | Yu-Sheng Chen | en |
| dc.contributor.author | 陳譽升 | zh_TW |
| dc.date.accessioned | 2021-05-16T16:19:11Z | - |
| dc.date.available | 2018-08-17 | |
| dc.date.available | 2021-05-16T16:19:11Z | - |
| dc.date.copyright | 2013-08-17 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5995 | - |
| dc.description.abstract | 在這篇論文中，我們利用電荷流體模型(Charged Fluid Model，CFM)來實現腦部核磁共振(Magnetic Resonance，MR)影像的分割，並針對電荷流體模型的演算法提出兩個新的權重參數，藉此改進原電荷流體模型演算法在目標物件輪廓模糊時的分割缺點。從概念上來說，電荷流體模型是一個模擬電荷流體行為的封閉曲線，它就像是液體一樣會流過或繞過各種不同的障礙，而在程序上我們將這個概念分成兩個步驟來進行。首先，我們將電荷分佈在特定的傳播界面(Propagating Interface)裡，並將電荷限制在此界面內達到靜電平衡。接著，第二步驟是根據影像強度的影響，將限制電荷分佈的傳播界面做適當的變形。一直重複這兩個步驟即可將曲線停留在目標物件的輪廓邊緣。我們使用此模型方法進行腦部核磁共振影像的分割，並使用數種影像資料庫進行實驗。實驗結果顯示本研究所提出的新權重參數，可以有效地改善原電荷流體模型演算法的分割效果。改進後的方法在各種模擬的雜訊狀況下皆具有相當不錯的結果。在臨床真實核磁共振影像的分割結果，亦有相當高的精準度。 | zh_TW |
| dc.description.abstract | In this thesis, we modify the Charged Fluid Model (CFM) to perform the segmentation of brain magnetic resonance (MR) images. We propose two new stopping forces for the CFM algorithm. Conceptually, the CFM is a simulation of charged fluid, which is like a liquid flowing through and around different obstacles. We divide the process into two steps. First, the CFM flows within the propagating interface until a specified electrostatic equilibrium is achieved. The second step is to evolve the propagating interface based on several image features. Those two procedures are repeated until the propagating front resides on the boundary of objects being segmented. We used this new model for brain MR image segmentation and conducted experiments using a large number of image volumes. The results showed that the new stopping forces can effectively improve the CFM algorithm to segment noisy images as well as real brain MR images. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-16T16:19:11Z (GMT). No. of bitstreams: 1 ntu-102-R00525047-1.pdf: 4576381 bytes, checksum: 9c7243d50b128c75021ed8901f5677f5 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 摘要 i
ABSTRACT ii 目錄 iii 圖目錄 v 表目錄 vi 符號表 vii 第1章 緒論 1 1.1 研究背景 1 1.2 研究目的 2 1.3 論文架構 3 第2章 文獻探討 4 2.1 可變形模型 4 2.2 水平集演算法 5 2.3 不使用邊緣偵測器的主動式變形輪廓 6 2.3.1 Mumford-Shah方程式 8 2.3.2 非邊緣偵測的水平集方法 9 2.4 腦表面擷取模型 11 2.5 大腦擷取工具 12 第3章 研究設計及方法 13 3.1 電荷流體模型 13 3.1.1 電荷流體模型的系統架構 13 3.1.2 靜電場方程式 15 3.1.3 影像梯度力場方程式 19 3.1.4 減偶極架構 19 3.1.5 電位計算 21 3.1.6 邊界元素擷取 22 3.1.7 平均電場 22 3.2 權重參數 23 3.3 演算法步驟 26 3.3.1 電荷分佈 26 3.3.2 變形傳播界面 27 第4章 實驗結果及討論 28 4.1 實驗說明 28 4.2 實驗結果 29 4.2.1 效果呈現 29 4.2.2 SBD 3mm影像資料庫 31 4.2.3 IBSR影像資料庫 32 第5章 結論與未來展望 43 5.1 結論 43 5.2 建議及未來方向 44 參考文獻 45 | |
| dc.language.iso | zh-TW | |
| dc.title | 使用改良的電荷流體模型實現腦部核磁共振影像的大腦擷取 | zh_TW |
| dc.title | Segmentation of Brain MR Images Using an Improved Charged Fluid Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃乾綱(Chian-Kang Huang),張瑞益(Ruei-Yi Chang),江明彰(Ming-Chang Chiang) | |
| dc.subject.keyword | 電荷流體模型,影像分割,可變形模型,核磁共振影像, | zh_TW |
| dc.subject.keyword | Charged Fluid Model,image segmentation,deformable model,magnetic resonance image, | en |
| dc.relation.page | 47 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2013-08-12 | |
| 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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| ntu-102-1.pdf | 4.47 MB | Adobe PDF | 檢視/開啟 |
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