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研究生:鄭博順
研究生(外文):Po-Shuen Cheng
論文名稱:超音波影像量測系統--肱動脈血管擴張反應之分析
論文名稱(外文):Ultrasound Measuremnet System – Brachial Flow-Mediated Dilation Response Analysis
指導教授:張艘
指導教授(外文):Fok-Ching Chong
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:106
中文關鍵詞:霍氏轉換邊緣保留濾波器血管內皮擴張收縮弁斑紋雜訊濾除梯度霍氏轉換
外文關鍵詞:Hough Transformspeckle noiseFMDedge-reserved filterFlow-mediated dilationgradient-based Hough Transform
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非侵入式的血管特性偵測方式,是為了達到早期發現早期治療的目的,透過不同方式的量測,以評估血管特性,判斷血管是否異常。
超音波對於血管弁鉞�估的提供了迄今唯一一種測定血管內皮弁鄋漱隤k。血管內皮作爲血液與平滑肌之間的仲介,對維持血管內環境的平衡具有重要作用,一氧化氮(NO)爲其主要活性因子。釵h心血管疾病的致病危險因素均是通過致內皮弁鈮l害而誘發臨床病變,如高膽固醇、高血壓、冠心病、糖尿病、吸煙等都可引起內皮弁鉬棆炕A使NO合成和釋放降低。
在本論文中,建構了一套量測血管管徑的超音波影像分析演算法來量測血管管徑變化,以代替傳統的人工手動量測,藉此測定血管內皮弁遄C我們使用超音波B-mode模式,以影像擷取卡每秒擷取10張影像,量測未截止血流前的平均管徑,與截止血流放開後的最大管徑,以評估血管內皮擴張收縮弁?flow—mediated dilation,FMD)。
先利用Hough Transform來偵測可能的血管中心位置,取20x20 pixels 的影像,再使用邊緣保留濾波器來有效地將影像的班紋雜訊濾除,並將影像中的邊緣訊號保留住,再利用Laplacian of Gaussian 作邊緣偵測。最後利用梯度霍氏轉換(Gradient-based Hough Transform),將五維的橢圓參數分解成二維空間的參數轉換,先找出中心位置之二維參數,再依序找出長軸、短軸、傾斜角度。用與近似的橢圓同面積的圓決定其血管半徑。
量測八個人的FMD值,受測者年齡分佈在23∼26歲,所得的結果,與人工估算的結果比較的話,平均FMD估算的準確度約為96.31%。
Non-invasive cardiovascular measurements are used to evaluate cardiovascular diseases early and to cure early. By using various evaluation methods of cardiovascular features to diagnose cardiovascular disease.

Flow-mediated dilation (FMD) evaluated by Ultrasound offers a mechanism to characterize endothelial function and, therefore, may play a role in the diagnosis of cardiovascular diseases. Cardiovascular endothelial is intermedia between blood and cardiovascular muscle. It has important effect for keeping balance of the environment in vessel. Nitric Oxide (NO) is the most important fact. Many cardiovascular diseases, like high cholesterol, high blood pressure, coronary heart disease ,diabetes, smoking , is the cause of endothelial dysfunction.

We design a series of images analysis methods to compute the change of cardiovascular radius in ultrasound images for evaluating endothelial function instead of measuring by-hand. We use B-mode ultrasound system to measure brachial cardiovascular radius. Then we capture the images by image capture card 10 images per second. We evaluate the average radius of brachial cardiovascular radius before we cut off blood flow. And we evaluate the maximum radius of brachial cardiovascular radius after we cut off blood flow and release it. Using the two radii we can compute the FMD value to evaluate endothelial function.

First, we use Hough Transform to detect possible center possible of vessel. Then we use edge-reserved filter to filtrate speckle noise and preserve edge. After filtrating noise, we utilize Laplacian of Gaussian to detect edge-points. Finally, we use gradient-based Hough Transform to determine the center position, major axis, minor axis, and angle of evaluated ellipse. We use radius of the circle witch has equal area with the evaluated ellipse to be our final result.

We measure the FMD value for 8 people (age from 23 to 26 years old). Comparing the result measured by our design system with by hand, the accuracy is 96.31%.
第一章 研究背景與動機 9
1-1 研究背景 9
1-1-1動脈脈壓 10
1-1-2動脈波傳導速度(pulse wave velocity,PWV) 10
1-1-3 MRI (Magnetic Resonance Imaging) 11
1-1-4 超音波評估動脈結構和彈性 12
1-1-5 超音波測定血管內皮弁?14
1-2 章節內容簡介 16
第二章 血管內皮弁?18
2-1動脈血管構造 18
2-2血管內皮弁?20
2-3 FMD的超音波檢測方式 22
第三章 量測原理 24
3-1超音波影像原理 24
3-1-1 Amplitude mode,A-Mode 25
3-1-2 Brightness mode,B-Mode 27
3-1-3 Constant Depth mode,C-Mode 30
3-1-4 Motion mode,M-Mode 30
3-1-5 Doppler,D-Mode 30
3-1-6彩色Doppler流動影像 32
3-1-7 彩色Doppler必v模式影像 33
3-2系統的開發工具 34
3-3 HOUGH TRANSFORM (HT) 37
3-3-1 Hough Transform 應用於直線上的偵測 37
3-3-2 Hough Transform 應用於圓形上的偵測 39
3-4 EDGE RESERVE FILTER 42
3-4-1 Bilateral Filter 45
3-4-2 血管的超音波圖形加上Speckle的模擬 48
第四章 分析流程 52
4-1 圓心位置的偵測 53
4-2 影像中SPECKLE的濾除 59
4-3 影像灰階值的調整 60
4-3-1指數型轉換 61
4-3-2對數型轉換 62
4-3-3以灰階統計圖為基礎轉換 63
4-4 影像邊緣偵測 68
4-4-1 Gradient Operator 68
4-4-2 Laplacian Operator 69
4-4-3 Laplacian與Laplacian of Gaussian的比較 74
4-5 血管中心點的偵測 77
4-6橢圓長軸、短軸、傾斜角度的偵測 84
第五章 實驗結果分析及結論 87
5-1實驗結果 87
5-2 處理連續影像時演算法的修正 92
5-2-1 灰階值的調整 92
5-2-2 中心點偵測的修正 92
5-2-3 邊緣偵測的修正 95
5-2-4 血管中心點偵測的修正 96
5-2-5 血管長軸、短軸判定的修正 98
5-2-6 觀測的區塊大小及位置的調整 99
5-3結論 100
5-4 未來工作 101
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[17] C. Tomasi, R. Manduchi, "Bilateral filtering for gray and color images", Sixth International Conference on Computer Vision, pp. 839-46. New Delhi, India, 1998.
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