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系統識別號 U0026-2408202012165500
論文名稱(中文) 基於斑紋對手持式超音波使用卷積神經網路的三維定位方法
論文名稱(英文) Freehand Ultrasound Three-dimensional Localization Approach Based on Speckle Pattern Using Convolutional Neural Network Models
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 108
學期 2
出版年 109
研究生(中文) 謝敏正
研究生(英文) Ming-Jeng Hsieh
學號 P76061069
學位類別 碩士
語文別 英文
論文頁數 49頁
口試委員 指導教授-王士豪
口試委員-吳佳慶
口試委員-梁勝富
口試委員-廖峻德
口試委員-林奕勳
中文關鍵字 解斑紋相關性  卷積神經網路  相對定位  徒手三維超音波定位 
英文關鍵字 speckle decorrelation  convolutional neural network  relative localization  freehand 3-D ultrasound localization 
學科別分類
中文摘要 在非破壞性超音波檢測中,如何在不使用輔助設備的情況下進行徒手三維定位以及重建是一門值得探討的議題。過去的研究結果三維超音波影像將二維超音波影像利用影像匹配以及解斑紋相關性達到相對定位的結果。然而利用解斑紋相關性輸出直平面距離受限於斑紋之間的相關係數。本研究目的在於使用動態追蹤演算法(KLT)進行影像匹配後,對斑紋紋理利用深度學習的訓練改善傳統解斑紋相關係對於預測垂直平面距離的準確率。其中利用非等方擴散濾波器以及Nakagami 模型參數對斑紋進行斑紋狀態分析。使用步進馬達帶動商用超音波儀器搭配12MHz 探頭掃描商用組織假體驗證定位結果。結果顯示在沒有軸向與側向位移的干擾下,解斑紋相關性與使用深度學習模型所得到的平均絕對誤差為0.073mm以及0.020mm。在同時移動三軸前提下,解斑紋相關性與使用深度學習模型所得到的平均絕對誤差為0.165mm以及0.130mm。本篇研究提供基於超音波紋理對相對定位達到徒手式三維重建的可行性。
英文摘要 In non-destructive ultrasound testing, how to perform 3D localization and reconstruction without using localization equipment is a worthy of being topic discussed. The previous studies have used image registration method and speckle decorrelation to achieve relative localization from 2-D slices to 3D reconstruction. However, the output of elevational distance using speckle decorrelation is limited by the correlation coefficient between frame-to-frame. Therefore, the purpose of this study is using in-plane motion algorithm (KLT) to detect and registration. Then using deep learning model training improve the accuracy of the conventional speckle decorrelation. Simultaneously, using anisotropic diffusion filter and Nakagami distribution model analysis scatterer concentration in speckle. This study used stepper motor and commercial ultrasonic scanner with a 12MHz linear probe to scan the tissue phantom to verify the localization results. The results show that without the interference of lateral and axial motion, the mean absolute error of speckle decorrelation method and deep learning model are 0.073mm and 0.020mm. Under the premise of moving the 3 axes at the same time, the mean absolute error of speckle decorrelation and deep learning model is 0.165mm and 0.130mm. This study provides a feasible image-based localization method to achieve 3-D freehand ultrasound.
論文目次 審查資格證明書 I
摘要 III
ABSTRACT IV
致謝 V
CONTENT VI
LIST OF FIGURES VIII
CHAPTER 1 INTRODUCTION 1
1.1 Foreword 1
1.2 Research Background 2
1.2.1 Non-destructed Testing 2
1.2.2 3-D Ultrasound Imaging 2
1.2.3 Localization 3
1.3 Research works 6
1.4 Research Objectives 7
CHAPTER 2 BACKGROUND 8
2.1 Fundamentals of Ultrasound 8
2.1.1 Fundamentals of Wave Propagation 8
2.1.2 Reflection, Refraction 8
2.1.3 Attenuation 10
2.2 In-plane Motion Tracking 11
2.2.1 Normalized Cross Correlation 11
2.2.2 Optical Flow Method 11
2.2.3 Kanade-Lucas-Tomasi Tracking Method 12
2.3 Speckle Detection 14
2.3.1 Speckle in Ultrasound Image 14
2.3.2 Fully Developed Speckle and Rayleigh Distribution 14
2.3.3 The Improved by Nakagami Distribution 16
2.3.4 Compression in Ultrasound Image 17
2.4 Out-of-plane Motion Estimation 19
2.4.1 Speckle Decorrelation 19
2.4.2 Decorrelation Curve 20
2.4.3 Deep Learning Model 21
CHAPTER 3 MATERIALS AND METHODS 23
3.1 In-plane Motion Tracking Method 23
3.1.1 Normalized Cross Correlation 23
3.1.2 KLT Method Based on LK Optical Flow 23
3.2 Speckle Detection 25
3.2.1 Filtering Structural Pattern 25
3.2.2 Detection Fully Developed Speckle Using Nakagami Distribution 26
3.3 Out-of-plane motion tracking method 27
3.3.1 Decorrelation Curve 27
3.3.2 Deep Learning Model 27
3.4 Verification of Image-based Localization Method 28
CHAPTER 4 RESULTS AND DISSCUSSION 31
4.1 In-plane motion Estimation Results 31
4.2 Speckle Detection Results 34
4.2.1 Fully Developed Speckle after Compression 34
4.2.2 Filtering Structural Pattern 35
4.3 Out-of-plane Motion Estimation Results 38
4.3.1 Conventional Decorrelation Curve Model 38
4.3.2 Deep Learning Model 39
4.4 3-D Reconstruction Results 43
CHAPTER 5 CONCLUSIONS 45
5.1 Conclusion 45
5.2 Future Works 46
REFERENCES 47

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