||Two-Staged Block Matching for Motion Tracking in 2D and 3D Musculoskeletal Ultrasonic Image Sequences
||Institute of Medical Informatics
Ultrasonic image sequences
principal component analysis
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.
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
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