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系統識別號 U0026-3108201713445500
論文名稱(中文) 手部疾病之橫切與縱切面超音波影像序列的組織追蹤
論文名稱(英文) Tissue Tracking in Transverse and Longitudinal Ultrasound Image Sequences for Hand Diseases
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 105
學期 2
出版年 106
研究生(中文) 林敬哲
研究生(英文) Ching-Che Lin
學號 p76044651
學位類別 碩士
語文別 英文
論文頁數 72頁
口試委員 指導教授-孫永年
口試委員-陳定宏
口試委員-柯建全
口試委員-蔡清欉
口試委員-王士豪
中文關鍵字 超音波  正中神經  肌腱  腕隧道症候群  板機指  追蹤  光流法  區塊匹配  主動輪廓模型  支援向量機 
英文關鍵字 ultrasound  median nerve  tendon  carpal tunnel syndrome  trigger finger  tracking  optical flow  block matching  active contour model  support vector machine 
學科別分類
中文摘要 腕隧道症候群和板機指為常見的手部疾病,而超音波系統在軟組織的功能和臨床病理診斷上已有廣泛的應用。近年來的研究指出,正中神經的位移和變形在腕隧道症候群患者和健康成人之間有明顯的差異,而臨床上肌腱的位移則為診斷板機指患者的重要依據之一。此外研究也發現腕隧道症候群和板機指經常發生在同一位患者上。然而在超音波影像序列中,組織位移並不一定平行於超音波探頭,而且斑點雜訊和其他外在因素等影響使得手動追蹤和測量更加困難且主觀。
本篇研究提出了兩種不同的追蹤方法,分別針對超音波影像序列上橫切面的正中神經和縱切面的肌腱組織進行追蹤。在正中神經方面,本篇提出之演算法使用機器學習方法在第一幀上先定位出正中神經的位置,再利用光流法和主動輪廓模型進行正中神經輪廓的追蹤。而在肌腱組織上,提出的方法結合光流法和區塊匹配方法,先利用光流法提供大致的估計位移,再由區塊匹配方法在超音波影像序列中計算出最佳的肌腱位移。
兩種提出的追蹤方法皆利用專家的手動追蹤結果進行評估。在正中神經的追蹤方法上,提出的方法在輪廓上有平均約0.88的相似度和4.46像素的輪廓平均誤差,而整體位移上正中神經中心點的誤差約為3.52像素。另外在肌腱追蹤的部分,追蹤方法先利用假體超音波影像序列進行驗證並和其他傳統方法比較,實驗結果顯示提出方法得出的結果較接近手動圈選結果且較其他方法穩定。
未來此方法可應用在臨床診斷上,觀察病患和健康成人的組織面積變化與位移等參數,並藉由這些參數區分疾病的嚴重程度。
英文摘要 In clinical diagnosis, ultrasound is an important technique and has been widely used for many common hand diseases such as carpal tunnel syndrome (CTS) and trigger finger. Recent studies show that the displacement and deformation of the median nerve and the tendon between healthy subjects and patients have significant difference. Moreover, CTS may occur with trigger finger patients more often. However, some problems, such as speckle noise, out-of-plane, etc., make it hard to track and measure manually in the ultrasound images.
This study presents two novel tracking strategies for the median nerve and the tendon in transverse and longitudinal view, respectively. To track the contour of the median nerve in the traverse ultrasound image sequence, the proposed method adopts the machine learning method for localization; then optical flow and active contour model are used to track and refine the contour in the ultrasound image sequences. To track the motion of the tendon, the proposed method integrates optical flow and block matching method to calculate the optimal tendon motion between ultrasound image frames.
In median nerve tracking, the accuracy of the proposed method is about 0.88 in average Dice similarity coefficient, 4.46 pixels in average mean of absolute difference, and 3.52 pixel for average center difference. In tendon tracking, the proposed method is validated by the phantom ultrasound sequence and compared with some classical tracking methods. The experimental results reveal that the proposed method is better and more stable than the comparative methods in most cases.
In the future, the proposed methods can further be applied in patient data to obtain clinical parameters such as the area and velocity of the tissues. By comparing the parameters between patients and normal subjects, the indexes use to distinguish the symptomatic and asymptomatic can then be defined.
論文目次 摘要 I
ABSTRACT III
誌謝 V
CONTENTS VI
LIST OF TABLES VIII
LIST OF FIGURES IX
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Related Work 3
1.3 Overview of the Proposed Methods and Thesis Organization 6
CHAPTER 2 Experimental Materials 8
2.1 Instruments 8
2.2 Experiment Setting at the Wrist 9
2.3 Experiment Setting at the Finger 10
CHAPTER 3 Tracking Method for Median Nerve 12
3.1 Overview 12
3.2 Preprocessing 14
3.3 Median Nerve Localization 15
3.3.1 Training Procedure 17
3.3.2 Predicting Procedure 19
3.4 Control Point Refinement 20
3.4.1 Active Contour Model 20
3.4.2 Outlier Removal 24
3.5 Motion Estimation 26
3.5.1 Optical Flow 26
3.5.2 Lucas-Kanade Method 27
3.5.3 Optical Flow with Pyramid Structure 28
3.6 Point Interpolation 30
CHAPTER 4 Tracking Method for Tendon 31
4.1 Overview 31
4.2 Motion Estimation 33
4.2.1 Pyramidal Optical Flow 33
4.2.2 Dominant Flow Extraction 34
4.3 Optimal Motion Determination 36
CHAPTER 5 Experimental Results and Discussions 38
5.1 Experimental Results for the Median Nerve 40
5.1.1 Evaluation of the Contour Tracking for the Median Nerve 41
5.1.2 Evaluation of the Displacement for the Median Nerve 42
5.1.3 Comparison of the Tracking Methods for the Median Nerve 46
5.2 Experimental Results for the Tendon 47
5.2.1 Validation using Standard Ultrasound Phantom 48
5.2.2 Evaluation of the Tendon in the Carpal Tunnel 50
5.2.3 Evaluation of the Tendon in Fingers 58
CHAPTER 6 CONCLUSION 62
6.1 Conclusions 62
6.2 Future Work 63
REFERENCES 64
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