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研究生:蔡瑛哲
研究生(外文):TSAI, YING-CHE
論文名稱:心血管影像序列之自動化血管切割與血流測量
論文名稱(外文):Automatic Vessel Segmentation and Blood Flow Measurement Using Angiogram Sequences
指導教授:李錫堅李錫堅引用關係
指導教授(外文):LEE, HSI-JIAN
口試委員:李錫堅黃仲陵范國清林信鋒潘健一
口試委員(外文):LEE, HSI-JIANHUANG, CHUNG-LINFAN, KUO-CHINLIN, SHIN-FENGPAN, JIANN-I
口試日期:2017-06-20
學位類別:博士
校院名稱:慈濟大學
系所名稱:醫學科學研究所
學門:醫藥衛生學門
學類:醫學學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:92
中文關鍵詞:血管切割血流測量
外文關鍵詞:vessel segmentationblood flow measurement
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冠狀動脈影像攝影已經成為心血管疾病的重要處置工具。本研究目的為發展一個電腦輔助方法,可以從血管影像序列中自動化的切割血管與計算血流張數。我們的方法包含:血管切割與血流張數測量。在切割方面,我們提出一個自適應性特徵轉換方法去改善血管特徵反應,這方法利用尺度因子去處理血管灰階變化,且透過灰階轉換,解決對比度差及灰階分布不均的缺點,接著是利用連通標記法去除其他組織與雜訊,此方法可以將血管結構擷取出來。在測量血流張數部份,我們提出一個自動化且非侵入式的血流張數計算方法,這方法包含2個主要元件:血管結構截取與血流張數測量。首先,我們使用一個自適應特徵轉換的方法去取得血管影像,並將血管影像切割為m × n血管區塊,經由一個多區塊投票方法去計算血管區塊數量並產生血流的時間密度曲線。為了增加測量準確性,我們從時間密度曲線上取前12個血管區塊的平均作為門檻值S,當有一張影像的血管區塊數量達到S值時,表示該張影像有最多的血管區塊,同樣的,我們也找出最少的血管區塊及所對應出是屬於第幾張影像,最後透過最多血管區塊的張數和最少血管區塊的張數差去計算血流張數。在我們的實驗中,血管切割的準確度為96.3%,自動血流張數計算與手動測量的相關性為0.874,其結果顯示提出的方法能準確切割血管與測量血流。
X-ray angiography has become an important treatment of cardiology diseases. The aim of this study is to develop a fully automatic computer-assisted method to extract vessel and computer blood flow frame count (BFFC) using angiograms sequence. The method included two parts: vessel segmenting and BFFC measurement. To extract vessel structures, we proposed an adaptive feature transformation function to improve the vesselness response. This method overcome numerous problems, which exist in the X-ray angiograms by using the scale factors and transformed intensities. Various scales were established to fit variations of the intensity distribution. The transformed intensities were applied to coping with lower contrast and nonuniform intensity distribution. Finally, the connected component labeling method was used to extract the vessels. The proposed method could distinguish between the vessel and the background in a complex background. To measure coronary blood flow, we used an adaptive feature transformation method to obtain vessel images. Subsequently, these images were segmented into m × n blocks. The blocks with a sufficiently higher percentage of pixels labeled “vessel” were defined as vessel blocks, which were counted from each vessel image. A time density curve (TDC) was then generated from the block count in each frame of an angiogram sequence. To increase the measurement precision, the mean of the top 12 vessel block numbers was defined as the stability value S from the TDC. The frame with the maximum number of vessel blocks was identified when the vessel block number of a frame reached the value S. A BFFC could be measured automatically as the frame number difference between the frames with the maximum and minimum numbers of vessel blocks. The proposed method could overcome the problems caused by user interaction, vessel intensity variation, and vessel motion. In our experiments, the accuracy of the proposed segmentation method was 96.3%. The Kappa value was 81.8%. The proposed BFFC method significantly correlated with manual measurement by a cardiologist (r = 0.874; P < 0.0001). The experiment result shows that the proposed method is feasible for vessel segmentation and BFFC measurement.


致謝 I
摘要 II
Abstract III
Table of Contents V
List of Figures VII
List of Tables XI
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Definition 6
1.3 Objective 7
1.4 Summary of Achievements 8
1.5 Organization of this Dissertation 10
Chapter 2 Related Work 11
2.1 Vessel Segmentation 11
2.2 Blood Flow Measurement 17
Chapter 3 Segmentation of Blood Vessels 22
3.1 High-Contrast Angiograms Extraction 23
3.2 Vessels Segmentation 28
Chapter 4 Measurement of Blood Flow 38
4.1 Vessel Extraction 39
4.2 Blood Flow Frame Count Measurement 40
Chapter 5 Experimental Results 44
5.1 Accuracy of High-Contrast Angiograms Extraction 44
5.2 Performance of Vessel Segmentation 45
5.3 Performance of Blood Flow Frame Count Measurement 58
Chapter 6 Discussion 62
6.1 Comparison of Vessel Segmentation Method 62
6.2 Comparison of Retinal Vessel Segmentation Method 64
6.3 Application of Computer-Assisted Blood Flow Measurement 66
6.4 Comparison of Entropy and Intensity 67
Chapter 7 Conclusion and Future Work 68
7.1 Conclusion 68
7.2 Future work 70
References 71


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