||Tissue Tracking in Transverse and Longitudinal Ultrasound Image Sequences for Hand Diseases
||Institute of Computer Science and Information Engineering
carpal tunnel syndrome
active contour model
support vector machine
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.
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
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