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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳中明(Chung-Ming Chen) | |
dc.contributor.author | Cheng-Pei Liu | en |
dc.contributor.author | 柳承沛 | zh_TW |
dc.date.accessioned | 2021-06-15T16:25:04Z | - |
dc.date.available | 2024-11-25 | |
dc.date.copyright | 2019-11-25 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-14 | |
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Chun-Ta Lin, Pulmonary Lobe Segmentation on CT Image, Master Thesis, National Taiwan University, July 2013. 25. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, Series B, vol. 39, no. 1, pp. 1-38, 1977. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52729 | - |
dc.description.abstract | 慢性阻塞性肺病(Chronic Obstructive Pulmonary Disease,COPD)是一種慢性發炎所造成的呼吸氣流阻塞(Airflow Limitation)疾病。患有慢性阻塞性肺病的病人,其工作能力及生活品質會大幅降低,嚴重者必須住院,進而造成病患家庭甚至是整體社會的社經方面的沉重負擔。而肺氣腫(Emphysema)則是造成慢性阻塞性肺病的一個重要原因。
以往慢性阻塞性肺病以及肺氣腫僅能依靠肺功能檢查(Pulmonary Function Test,PFT)與肺部電腦斷層掃描(Computer Tomography,CT)影像來診斷。然而,肺功能檢查對某些病人來說是相當辛苦的。因此,我們希望能在影像上取出有用的參數,發展出一套有效的評估指標,以期能夠以影像上得到的參數所發展出的指標,取代傳統的肺功能檢查來評估病情的發展。本研究利用將肺氣腫所影響的病變區域,經由三維數位影像處理的方法,並結合圖像形態學上的方法,以修正式期望值最大化演算法(Expectation Maximization,EM),將病變區域Fitting為球形Bullae,並藉由Bullae之尺寸及位置資訊,發展我們新開發的Mirage Index(MI)指標來評估肺氣腫的嚴重程度。 我們將MI指標值與文獻中常見的像素指標(Pixel Index,PI)相互比對,並使用評估氣流受阻的正規化肺功能參數FEV1/FVC當作嚴重程度指標進行相關係數分析,可以發現我們的MI指標,可將相關係數由原來像素指標對FEV1/FVC的-0.6333±0.1106,提升至-0.7532±0.0819。另外我們也針對各種不同的病例來進行分析比較,可以看出使用MI指標來進行分析可以比較正確地解釋肺功能的結果。綜合我們研究的結果,由於MI指標值可以根據Bullae大小尺寸與所在位置給予不同且較為精確的權重值,因此可以給予病患比較正確的肺氣腫病情評估結果。然而目前仍有部分病例以肺氣腫參數所發展的MI仍無法正確評估,期望未來若將吸氣吐氣間動態模型與支氣管參數加入考慮後,能發展出效果更佳之指標。 | zh_TW |
dc.description.abstract | Chronic Obstructive Pulmonary Disease (COPD) is an airflow limitation disease caused by chronic inflammation. COPD patients suffer from low quality life. Severe patients need to be hospitalized resulting in heavy loading in the whole society. Emphysema is one of the major causes of COPD.
In the past, COPD and emphysema rely on pulmonary function tests (PFTs) and computer tomography (CT) image for diagnosis. However, performing PFTs may be a hard task for some patients. Hence, we want to develop an effective assessment index from CT image parameters which can be used to assess the disease severity instead of using the traditional PFTs. In this study, we utilize 3D image processing techniques with morphologic methods, to fit emphysema lesion area into bullae balls by modified expectation maximization (EM) algorithm which we proposed in this thesis. Besides, we use the bullae size and location information to develop a new index, called Mirage Index (MI), to assess the severity of emphysema. We use FEV1/FVC as severity index which is a normalized parameter for evaluating airflow limitation. We compare the correlation coefficients of pixel index (PI) which is usually used in literature, and MI vs FE1/FVC, respectively. We can find that the correlation coefficient can be improved from -0.6333±0.1106 (by using PI) to -0.7532±0.0819 (by using MI). In addition, we also investigate some special cases. From the statistical analysis and special case study results, we can see, MI can offer better assessment in different analyses. By the research results, because the MI is developed by giving different weights to the bullae with different size class and location, the MI can result in better assessment. However, there are still some special cases cannot be properly assessed by MI. Therefore, we hope in the future we can develop a better index by considering the dynamic model between inspiration and expiation and bronchus parameters in image. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T16:25:04Z (GMT). No. of bitstreams: 1 ntu-104-R02548006-1.pdf: 23624264 bytes, checksum: 8489d129f60a5afbae2881019a784bd4 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 誌謝 ii
摘要 iii Abstract iv 目錄 v 圖目錄 vii 表目錄 viii 第一章 緒論 1 1.1研究背景 1 1.2研究動機與目的 6 1.3論文架構 7 第二章 文獻探討 8 2.1肺功能相關參數與電腦斷層掃描影像參數研究探討 8 2.2肺氣腫電腦斷層掃描影像相關研究 10 2.3肺氣腫評估指標相關研究 11 第三章 影像處理演算法與肺氣腫評估指標開發 12 3.1影像處理演算法流程 13 3.1.1 Bullae Fitting演算法 14 3.1.2修正式期望值最大化演算法 17 3.2肺氣腫評估指標開發 22 3.2.1肺氣腫Bullae尺寸與位置分類方法 22 3.2.2指標權重值訓練 24 第四章 影像處理與指標開發結果及相關性研究 26 4.1三維電腦斷層掃描影像處理之結果 26 4.1.1連通區域之Fitting結果 26 4.1.2整體肺部區域之Bullae Fitting結果 28 4.2肺氣腫評估指標與肺功能檢查參數之相關性探討 32 4.2.1肺氣腫評估指標之開發與相關性分析 32 4.2.2肺氣腫評估指標之效果分析 37 4.2.3肺氣腫評估指標之特例分析 43 第五章 結論與未來發展 50 5.1結論 50 5.2未來發展 51 參考文獻 52 | |
dc.language.iso | zh-TW | |
dc.title | 肺氣腫之肺功能測試參數及電腦斷層掃描影像參數之相關性分析 | zh_TW |
dc.title | Correlation Analysis between Pulmonary Function Test Parameters and Computer Tomography Image Parameters of Emphysema | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 余忠仁,張允中,孫永年 | |
dc.subject.keyword | 肺氣腫,肺功能檢查,電腦斷層掃描影像,三維影像處理,相關性分析, | zh_TW |
dc.subject.keyword | Emphysema,PFT,CT image,3D image processing,Correlation analysis, | en |
dc.relation.page | 54 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-14 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
顯示於系所單位: | 醫學工程學研究所 |
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