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系統識別號 U0026-2108201214110600
論文名稱(中文) 以模型為基礎之分割方法於多種型態醫學影像之應用
論文名稱(英文) Model-Based Segmentation Methods with Application to Multimodality Medical Image Data
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 100
學期 2
出版年 101
研究生(中文) 陳昕辰
研究生(英文) Hsin-Chen Chen
學號 p78961338
學位類別 博士
語文別 英文
論文頁數 143頁
口試委員 指導教授-孫永年
召集委員-高材
口試委員-蘇芳慶
口試委員-李同益
口試委員-周一鳴
口試委員-李建德
口試委員-連震杰
口試委員-吳育德
口試委員-吳宗憲
中文關鍵字 動態形狀模型  鏈結模型  胎兒假體  手指關節機制  賈伯特  手骨運動模擬  模型為基礎之分割  核磁共振影像  多型態影像  量化量測  對位  動態輪廓模型  三維超音波  X光影像 
英文關鍵字 active shape model  articulated model  fetal phantom  finger joint mechanism  Gabor features  hand bone motion animation  Insall-Salvati Ratio  model-based segmentation  MR  multimodality image  quantitative measurement  registration  snake  3D ultrasound  X-ray image 
學科別分類
中文摘要 解剖構造的分割能提供詳細的解剖訊息並有利於後續之量化分析,所以在醫學影像相關的研究中是一項基礎且重要的工作項目。然而不同解剖構造之間存在著顯著的變異性,包含形狀、姿勢、構造周遭環境,以及運動學特性上。除此之外,各型態的醫學影像也呈現了不同的灰階特性。這些觀察顯示出醫學影像分割屬於解剖構造相依與影像型態相依的問題,很難單純地利用影像為基礎的特徵(如:灰階梯度或亂度)得到有效的解答。本篇論文致力於設計以模型為基礎的分割方法,並應用它們於三個傳統分割方法無法解決之解剖構造量化量測的研究議題。
第一個研究議題為三維超音波影像之胎兒頭部分割與顱面量化量測。為了克服超音波影像中低對比度以及頭部模糊邊界之困難點,我們提出的分割方法採用了胎兒假體頭部形狀資訊做為分割時的參考,並自動化地找出影像與模型在解剖上的對應(眼睛與頭骨)來進行全域以及區域性模型與影像貼合程度最佳化。本論文中的第二個研究議題為多姿勢核磁共振影像之手骨分割與運動模擬。其中,手部複雜的鏈結構造以及多運動自由度造成在多姿勢影像中手部姿勢相當大的變異。我們因此提出一個基於可驅動鏈結模型之分割演算法來克服這樣的問題。我們提出的模型演算法包含了指骨關節機制,骨塊表面,以及影像灰階模板,來分割骨塊並且模擬骨塊之運動。第三個研究議題為應用標記點定位於側照膝關節X光影像之Insall-Salvati Ratio自動化量測。我們設計了一個新的對位輔助之動態形狀模型架構,其中形狀模型將先被自動地擺放於影像中,接著透過本論文提出的模型貼合策略來定位出標計點位置。我們提出的方法能有效地克服X光影像標記點定位之困難點,如:目標物邊界不穩定之灰階外觀。
定性與定量實驗皆被實現來驗證本論文提出之分割方法準確性。此外,我們也進行了與現有的模型為基礎之分割方法(如:動態輪廓模型、Chan-Vese 等位階函數模型、動態形狀模型)之比較,結果顯示出本論文提出之方法能提供較準確之分割結果。更重要的是,這三個研究議題之結果傳達出相同的概念-利用特定解剖模型之知識(如:形狀、運動學或影像之特性)去適當地描繪影像中的變異內容,可以在不同型態醫學影像的分割問題中得到好的解答。本論文提出的方法有助於研究人員有效率地進行後續的量化分析,並具有好的延伸性於不同解剖構造的分割問題以及電腦輔助診療系統的設計。
英文摘要 Segmentation of anatomical structures is a fundamental and critical task in medical image-related research because it can provide anatomical details which facilitate follow-up quantitative analysis. However, there exist significant variations of shape, pose, neighborhood context, and kinematic property among different anatomical structures. In addition, diverse intensity characteristics are presented inherently in multimodality image data. These observations indicate that medical image segmentation is an anatomy/modality-dependent problem, which is difficult to be handled by purely using generic image-based features, e.g., intensity gradient or entropy. This thesis is dedicated to designing model-based segmentation methods for three quantitative research issues, which are unable to solve by existing segmentation methods.
The first conducted research is fetal head segmentation for quantitative measurement of craniofacial structure from three-dimensional (3D) ultrasound images. To cope with difficulties such as poor intensity contrast and fuzzy head boundaries in ultrasound images, the proposed model-based segmentation method adopts a fetal phantom to provide the reference shape information, and automatically derives anatomical correspondences (i.e., eyes and skull) for global and local shape optimization between the model and image. As to the second research that is hand bone segmentation and motion animation for multi-postural magnetic resonance (MR) images, we encounter a major difficulty of pose variation among postural images, which is originated from complex articulation and multiple degrees of freedom (DoF) of hand. We hence propose another segmentation method based on a drivable articulated model, containing finger joint mechanism, bone surfaces, and image intensity patterns, to segment and animate hand bones. The third research is landmarks localization on lateral knee X-ray images for automatic measurement of Insall-Salvati Ratio (ISR). We design a new registration-assisted active shape model (RAASM) framework, in which automatic model positioning is first achieved and then, landmarks are localized based on the RAASM fitting process. Using the proposed method, difficulties such as fluctuant intensity appearances nearby the boundary of target structure are efficiently resolved.
Both qualitative and quantitative experiments were carried out to validate the accuracy of the proposed segmentation methods. Moreover, comparison studies with several conventional model-based methods, such as snake, Chan-Vese level-set model, and active shape model, were conducted to demonstrate superiority of our algorithms. More importantly, the outcomes from the three different research issues convey the same concept that is, incorporating anatomy-specific models with different types of prior knowledge (e.g., shape, kinematic or image properties), which adequately characterizes the variations appearing in the processed images, can achieve accurate segmentation in different types of imaging modalities. The proposed methods are helpful for researchers to conduct follow-up quantitative analysis, provide a good extensibility to segmentation of other anatomies, as well as facilitate the design of related computer-aided diagnosis systems.
論文目次 摘要 I
Abstract III
Acknowledgements V
List of Figures XI
List of Tables XVII
Chapter 1 Introduction 1
1.1 Motivations 1
1.2 Statement of Research Issues in This Thesis 2
1.2.1 Research I: Fetal Head Segmentation for Quantitative Measurement of Craniofacial Structure from 3D Ultrasound Images 2
1.2.2 Research II: Hand Bone Segmentation from Multi-Postural MR images for Motion Animation 4
1.2.3 Research III: Landmark Localization on Lateral Knee X-Ray Images for Automatic Measurement of Insall-Salvati Ratio 6
1.3 Contributions 8
1.4 Overview of Chapters 11
Chapter 2 Technical Background 13
2.1 Medical Image Modalities 13
2.1.1 X-Ray 13
2.1.2 Computed Tomography 14
2.1.3 Magnetic Resonance 15
2.1.4 Ultrasound 15
2.2 Medical Image Segmentation 16
2.2.1 Pixel Classification-Based Methods 17
2.2.2 Region-Based Methods 18
2.2.3 Deformable Model-Based Methods 20
2.2.4 Registration-Based Methods 22
2.3 Related Quantitative Analysis 24
2.3.1 Measurement of Fetal Biometric Parameters 24
2.3.2 Articulated Structure Segmentation and Motion Animation 26
2.3.3 Bony Landmark Localization 28
Chapter 3 Image Preparation and Descriptions 30
3.1 Fetal 3D Ultrasound Images and Craniofacial Measurements 30
3.2 Hand Multi-Postural MR Images 31
3.3 Lateral Knee X-Ray Images and Definitions of ISR Landmarks 34
Chapter 4 Image-Driven Model Construction 37
4.1 Head Surface Model from 3D Ultrasound Images of Fetal Phantom 37
4.1.1 Phantom Description 37
4.1.2 Phantom Image Segmentation and Surface Reconstruction 38
4.2 Drivable Articulated Model for Hand Multi-Postural MR Images 40
4.2.1 Model Architecture 41
4.2.2 Finger Joint Mechanism 42
4.2.3 Intensity Patterns 43
4.2.4 Joint Parameter Initialization 44
4.3 Patella Modeling for Lateral Knee X-Ray Images 45
4.3.1 Point Distribution Model 45
4.3.2 Intensity Appearance Model 48
Chapter 5 Registration-Based Segmentation for Object Boundary Extraction 50
5.1 Fetal Head Segmentation 50
5.1.1 Coarse-to-Fine Strategy for Eye Detection 51
5.1.2 Feature-Based Registration 56
5.1.3 3D Snake Deformation 59
5.2 Multi-Postural Hand Bone Segmentation 62
5.2.1 Articulated Registration 63
5.2.2 Surface Refinement 69
5.2.3 Joint Parameter Refinement 72
5.3 Patella Boundary Identification 75
5.3.1 Patella Feature Point Extraction 75
5.3.2 Registration-Assisted Active Shape Model 78
Chapter 6 Quantitative Measurement and Demonstration 84
6.1 Quantitative Measurements of Fetal Craniofacial Structure 84
6.2 Hand Bone Motion Animations and MR volume Morphing 86
6.3 Calculation of Insall-Salvati Ratio 87
Chapter 7 Experimental Results and Discussions 90
7.1 Research I: Fetal Head Segmentation for Quantitative Measurement of Craniofacial Structure from 3D Ultrasound Images 90
7.1.1 Accuracy Evaluation of Head Segmentation 90
7.1.2 Performance Evaluation of Measurement System 93
7.1.3 Statistical Analysis on Craniofacial Measurement 95
7.1.4 Discussions 96
7.2 Research II: Hand Bone Segmentation from Multi-Postural MR Images for Motion Animation 100
7.2.1 Visual Evaluation 100
7.2.2 Quantitative Analysis of Segmentation Results 102
7.2.3 Comparative Study 105
7.3 Research III: Landmark Localization on Lateral Knee X-Ray Images for Automatic Measurement of Insall-Salvati Ratio 107
7.3.1 Accuracy Evaluation on Landmark Locations 108
7.3.2 Accuracy Evaluation on Insall-Salvati Ratio 110
7.3.3 Performance Evaluation of Landmark Localization System 110
7.3.4 Comparative Study 111
7.3.5 Discussions 112
Chapter 8 Conclusions and Future Works 115
8.1 System Merits 115
8.2 System Limitations 117
8.3 Future Works 118
References 121
Curriculum Vitae 134
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