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系統識別號 U0026-2408201015355500
論文名稱(中文) 兩階段區塊匹配於二維與三維超音波影像序列之肌肉骨骼運動追蹤
論文名稱(英文) Two-Staged Block Matching for Motion Tracking in 2D and 3D Musculoskeletal Ultrasonic Image Sequences
校院名稱 成功大學
系所名稱(中) 醫學資訊研究所
系所名稱(英) Institute of Medical Informatics
學年度 98
學期 2
出版年 99
研究生(中文) 陳儀津
研究生(英文) Yi-Chin Chen
學號 Q5697404
學位類別 碩士
語文別 英文
論文頁數 110頁
口試委員 指導教授-孫永年
口試委員-程琡敏
口試委員-王世豪
口試委員-陳定宏
口試委員-劉育寰
中文關鍵字 超音波影像序列  運動追蹤  紋理特徵  主成分分析 
英文關鍵字 Ultrasonic image sequences  motion tracking  texture feature  principal component analysis 
學科別分類
中文摘要 因為超音波低成本及非侵入性與即時成像的特性,分析超音波影像序列之運動追蹤在臨床診斷中已經被廣泛的利用。在肌肉超音波影像中,由於軟組織形變所造成的非剛體運動往往增加運動估測的困難度。本論文中,針對二維肌肉超音波影像序列,我們提出了兩階段區塊匹配技術分析肌肉運動。這個方法是透過影像統計資料的選取及兩種大小不同匹配區塊的交替使用來提升影像追蹤準確度。使用紋理特徵與主成分兩種統計資料,能提供穩定的匹配兩區塊相似準則,甚至於運動較不穩定的範圍內,都能良好的確認與追蹤特徵顯著的區域。在第一階段,以較大的匹配區塊來掌握欲追蹤區域的全域資訊,並且挑選多個可能匹配的候選點。在第二階段,則在眾多候選點中以較小的匹配區塊比對欲追蹤區塊的區域變化,並找出最匹配的候選點。而我們提出了多樣板、次樣板及主成分分析等三種匹配準則供每個階段來使用。匹配時交替使用兩種大小不同的區塊,可在每一個運動追蹤的步驟中同時考慮到欲追蹤區域的全域資訊與區域變化。而從眾多候選點中挑選最佳候選點的兩階段式策略也可以提高追蹤的穩定度及準確度。再則,透過根據之前運動訊息來預測與修正的卡爾曼(Kalman)濾波器,可處理肌肉不穩定運動的問題。而在三維超音波影像中,同樣使用兩階段匹配。在第一個階段裡我們使用主成分分析做為匹配的準則,這是為了要克服因為低取樣率所造成的較大組織型變的問題。而第二階段,使用一半大小的樣板(而不是1/4大小)來做為匹配準則,主要是因為過小的樣板無法呈現欲追蹤區域的特徵。在實驗結果中,所提出的二維與三維超音波影像追蹤方法以活體肌肉骨骼超音波影像驗證,並與專家的評估比對。
英文摘要 Motion tracking by analyzing ultrasonic image sequences has become widespread in clinical diagnosis for its low cost, noninvasive, and real time imaging ability. Non-rigid motion in musculoskeletal ultrasonic image sequences increases the difficulty of motion estimation due to soft tissue deformation. According to the characters of 2D ultrasonic image sequences, two-staged block matching method through the selection of statistical information and the usage of alternate large and small matching block is proposed to estimate the muscular motion and can achieves high accuracy. Block matching technique based on the statistical information, such as texture features and principal components, can provide stable similarity criterion even on the ambiguity motion regions to determine and track the discrimination regions well. In the first stage, large matching block is used to handle the global information of matching pattern, and several matched candidates are chosen. In the second stage, the best candidate is selected from these candidates by comparing the local variation of matching pattern in small matching block. Three kinds of matching criteria, i.e. multi-template, sub-template, and principal component analysis, are developed and can be used in each matching stage. By using the large and small matching blocks alternatively, both global information and local variance can be taken into consideration simultaneously in the motion-tracking step. Two-staged strategy, which selects the best candidate from several candidates, can then obtain more accurate and stable tracking results. In addition, the Kalman prediction and correction based on previous motion information can be used to handle the problems of unstable movement. In 3D ultrasonic image sequence, two-staged block matching method is also used. In the first stage, the principal component analysis is used as the matching criterion in order to overcome the problem of large tissue deformation due to its low frame rate. In the second stage, the sub-template, with half (not quarter) of template size, is used as the matching template. It is because a too small template can not get the enough features to represent the matching region. In the experimental results, the accuracy is validated from in vivo musculoskeletal ultrasonic image sequences by comparing the results with the expert-defined ground truth.
論文目次 CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Relative Works 3
1.3 Overview of the Proposed Method and Thesis Organization 5
CHAPTER 2 MATERIALS 8
2.1 2D and 3D Ultrasound Imaging 8
2.2 2D and 3D Ultrasonic Image Properties 11
CHAPTER 3 2D MOTION TRACKING SYSTEM 18
3.1 System Architecture 18
3.2 Preprocessing 19
3.3 Texture Feature Extraction and Selection in 2D Image Sequence 22
3.3.1 Co-occurrence matrix 23
3.3.2 Neighborhood gray tone difference matrix 25
3.3.3 Texture feature selection via stepwise regression 27
3.4 Principal Components Training in 2D Image Sequence 31
3.4.1 Principal component analysis 31
3.4.2 Principal component transform 34
3.5 2D Motion Tracking Strategy 36
3.5.1 Pattern matching via principal components 38
3.5.2 Good discriminating position finding via principal components 43
3.5.3 Multi-template 44
3.5.4 Sub-template 45
3.5.5 Kalman filter 46
CHAPTER 4 3D MOTION TRACKING SYSTEM 52
4.1 System Architecture 52
4.2 Preprocessing 53
4.3 Texture Feature Extraction and Selection in 3D Image Sequence 54
4.4 Principal Components Training in 3D Image Sequence 57
4.5 3D Motion Tracking Strategy 60
4.5.1 Pattern matching via principal components 61
4.5.2 Sub-template 62
CHAPTER 5 EXPERIMENTAL RESULTS AND DISCUSSION 64
5.1 Ultrasound Equipment 64
5.2 Results of 2D in vivo Musculoskeletal Ultrasonic Image Sequences 64
5.2.1 Experiments of conventional block matching 66
5.2.2 Experiments of single stage block matching 72
5.2.3 Experiments of proposed 2D motion estimation algorithm 78
5.2.4 Results comparison between proposed method and other methods 89
5.3 Results of 3D in vivo Musculoskeletal Ultrasonic Image Sequences 91
5.3.1 Experiments of proposed 3D motion estimation algorithm 91
5.3.2 Experiments of simulated 2D image sequences 101
CHAPTER 6 CONCLUSIONS AND FUTURE WORK 106
6.1 Conclusions 106
6.2 Future Work 107
REFERENCES 108

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