심장 컴퓨터 단층촬영 영상으로부터 경사도 보조 지역 능동 윤곽 모델을 이용한 심장 영역 자동 분할 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 신영길.The heart is one of the most important human organs, and composed of complex structures. Computed tomography angiography (CTA), magnetic resonance imaging (MRI), and single photon emission computed tomography are widely used, non-invasive cardiac imaging modalities. Compared with other modalities, CTA can provide more detailed anatomic information of the heart chambers, vessels, and coronary arteries due to its higher spatial resolution. To obtain important morphological information of the heart, whole heart segmentation is necessary and it can be used for clinical diagnosis. In this paper, we propose a novel framework to segment the four chambers of the heart automatically. First, the whole heart is coarsely extracted. This is separated into the left and right parts using a geometric analysis based on anatomical information and a subsequent power watershed. Then, the proposed gradient-assisted localized active contour model (GLACM) refines the left and right sides of the heart segmentation accurately. Finally, the left and right sides of the heart are separated into atrium and ventricle by minimizing the proposed split energy function that determines the boundary between the atrium and ventricle based on the shape and intensity of the heart. The main challenge of heart segmentation is to extract four chambers from cardiac CTA which has weak edges or separators. To enhance the accuracy of the heart segmentation, we use region-based information and edge-based information for the robustness of the accuracy in heterogeneous region. Model-based method, which requires a number of training data and proper template model, has been widely used for heat segmentation. It is difficult to model those data, since training data should describe precise heart regions and the number of data should be high in order to produce more accurate segmentation results. Besides, the training data are required to be represented with remarkable features, which are generated by manual setting, and these features must have correspondence for each other. However in our proposed methods, the training data and template model is not necessary. Instead, we use edge, intensity and shape information from cardiac CTA for each chamber segmentation. The intensity information of CTA can be substituted for the shape information of the template model. In addition, we devised adaptive radius function and Gaussian-pyramid edge map for GLACM in order to utilize the edge information effectively and improve the accuracy of segmentation comparison with original localizing region-based active contour model (LACM). Since the radius of LACM affects the overall segmentation performance, we proposed an energy function for changing radius adaptively whether homogeneous or heterogeneous region. Also we proposed split energy function in order to segment four chambers of the heart in cardiac CT images and detects the valve of atrium and ventricle. In experimental results using twenty clinical datasets, the proposed method identified the four chambers accurately and efficiently. We also demonstrated that this approach can assist the cardiologist for the clinical investigations and functional analysis.Contents Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Goal 7 1.3 Main Contribtions 9 1.4 Organization of the Dissertation 10 Chapter 2 Related Works 11 2.1 Medical Image Segmentation 11 2.1.1 Classic Methods 11 2.1.2 Variational Methods 15 2.1.3 Image Features of the Curve 21 2.1.4 Combinatorial Methods 25 2.1.5 Difficulty of Segmentation 30 2.2 Heart Segmentation 33 2.2.1 Non-Model-Based Segmentation 34 2.2.2 Unstatistical Model-Based Segmentation 35 2.2.3 Statistical Model-Based Segmentation 37 Chapter 3 Gradient-assisted Localized Active Contour Model 41 3.1 LACM 41 3.2 Gaussian-pyramid Edge Map 46 3.3 Adaptive Radius Function 50 3.4 LACM with Gaussian-pyramid Edge Map and Adaptive Radius Function 52 Chapter 4 Segmentation of Four Chambers of Heart 54 4.1 Overview 54 4.2 Segmentation of Whole Heart 56 4.3 Separation of Left and Right Sides of Heart 59 4.3.1 Extraction of Candidate Regions of LV and RV 60 4.3.2 Detection of Left and Right sides of Heart 62 4.4 Segmentation of Left and Right Sides of Heart 66 4.5 Separation of Atrium and Ventricle from Heart 69 4.5.1 Calculation of Principal Axes of Left and Right Sides of Heart 69 4.5.2 Detection of Separation Plane Using Split Energy Function 70 Chapter 5 Experiments 74 5.1 Performance Evaluation 74 5.2 Comparison with Conventional Method 79 5.3 Parametric Study 84 5.4 Computational Performance 85 Chapter 6 Conclusion 86 Bibliography 89Docto

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