系統識別號 U0026-0812200915080652
論文名稱(中文) 基於模型之醫學影像運動分析
論文名稱(英文) Model Based Motion Estimation in Medical Image Sequences
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 97
學期 2
出版年 98
研究生(中文) 林正賢
研究生(英文) Cheng-Hsien Lin
學號 P7888101
學位類別 博士
語文別 英文
論文頁數 118頁
口試委員 口試委員-林康平
中文關鍵字 標記化磁振造影影像  細胞顯微影像  超音波影像  細胞追蹤  運動偵測 
英文關鍵字 cellular microscopic images  ultrasound images  tagged MRI  cell tracking  motion estimation 
中文摘要 運動分析在影像處理中,對於目標物的樣式識別經常有著相當重要的幫助。而在現今的研究中,應用於醫學影像的運動分析與物體追蹤也佔有重要的地位。然而,傳統的運動偵測法多是針對剛體運動而設計,因此並不一定適用於醫學影像中的運動分析。在本研究中,我們根據各種醫學影像的影像特性,提出加入以模型為基礎的運動偵測架構,分別針對超音波影像、標記化磁振造影影像以及細胞顯微影像,各自提出適用的運動分析演算法。
英文摘要 Motion analysis is very useful for recognizing target patterns from a sequence of images. Applications in motion estimation and target tracking become especially important in medical and biomedical researches nowadays. However, traditional methods which are optimal for rigid body motion are not suitable for medical analysis due to the object deformation and noise problems. In this study, we tried to propose adequate motion estimation methods for several medical motion applications which include motion field estimation from ultrasound images, tag line tracking from tagged magnetic resonance (MR) images, and live cell tracking from microscopic images.
Generally, the usual problems in medical motion analysis include: speckle noises and temporal de-correlation of the speckle patterns in ultrasound images; large motion and tag decaying problems in tagged MR images; and low contrast in pseudopods and topological changes in cellular microscopic images. To overcome these problems, it is necessary to integrate a priori knowledge based on the physical properties into the motion estimation process. In this study, we first designed a hierarchical maximum a posteriori estimator together with an ultrasonic feature model for ultrasound image sequences. A motion compounding method is also proposed to reduce speckle noises and to enhance image quality based on the proposed motion estimation method. To cope with the problems of large motion and tag decaying, we proposed to incorporate a cardiac motion model based prediction scheme and a candidate pre-screening technique together with the deformable models to track the tag lines. To segment and track highly deformable cells, we have presented an automatic method based on the framework of modified T-snakes coupled with the knowledge of cellular life model.
The proposed motion estimation methods were compared with several existing methods via a series of experiments with both simulated and clinical image sequences. Experimental results showed that motion could be accurately assessed in different types of imaging modalities. The proposed systems can help to perform better quantification and analyses in clinical applications. It will certainly help medical doctors to achieve better observation and more accurate assessments, and thus result in better diagnostic quality.
1.1 Motivation 1
1.2 Background and Related Works 4
1.2.1 Motion Analysis in Ultrasound Images 4
1.2.2 Cardiac Motion Analysis in Tagged MR Images 6
1.2.3 Cellular Motion Analysis in Microscopic Images 7
1.3 Overview of the Proposed Method and Thesis Organization 9
2.1 Overview 11
2.2 Block-Matching Algorithm and Maximum Likelihood Motion Estimation 12
2.3 Maximum A Posteriori (MAP) Motion Estimation 17
2.4 Hierarchical MAP Motion Estimation 18
2.5 Vector Post-Processing Using Adaptive Feature Weighted Filtering 22
2.6 Motion Compounding 27
3.1 Overview 32
3.2 Preprocessing 32
3.3 Active Contour Model for Tag Line Tracking 36
3.4 Temporal Prediction Using Motion Model 38
3.5 Candidate Pre-Screening 41
3.6 Strain Analysis and Visualization 42
4.1 Overview 44
4.2 Preprocessing 46
4.3 T-Snake 49
4.4 Recognition of Cellular State 52
4.5 Division Operator 57
5.1 Motion Estimation Results in Ultrasound Images 60
5.1.1 Synthetic Experiments 60
5.1.2 Clinical Experiments 69
5.2 Motion Compounding Results in Ultrasound Images 73
5.2.1 Motion-Simulated Phantom Experiments 73
5.3.2 Clinical Experiments 76
5.3 Motion Estimation Results in Tagged MR Images 84
5.3.1 Motion-Simulated Phantom Experiments 84
5.3.2 Clinical Experiments 85
5.4 Motion Estimation Results in Cell Tracking from Microscopic Images 87
6.1 Hierarchical Feature Weighted Motion Estimation and Motion Compounding 97
6.2 Motion Model Based Tag Line Tracking 99
6.3 Live Cell Tracking Based on Cellular State Recognition 102
VITA 116
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