系統識別號 U0026-2408201219200600
論文名稱(中文) 以模型為基礎的三維超音波影像分割於胎兒頭部與軀幹之量測
論文名稱(英文) Model-based Segmentation for Measurement of Head and Trunk Structures from 3D Fetal Ultrasound Images
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) 石慧萱
研究生(英文) Hui-Hsuan Shih
學號 P76994458
學位類別 碩士
語文別 英文
論文頁數 78頁
口試委員 指導教授-孫永年
中文關鍵字 三維  超音波胎兒影像  對位  Active Shape Model  以模型為基礎之分割 
英文關鍵字 3D  fetal ultrasound images  Active Shape model  registration  model-based segmentation 
中文摘要 超音波具有即時、低成本、容易使用以及非侵入性的優點,因此,在臨床上評估子宮內胎兒的生長情形大部分都使用超音波影像來進行產前檢查。透過觀察三維超音波影像中的胎兒之頭部以及軀幹整體結構,醫師可看到胎兒目前的整體成長狀態以及位於子宮內的姿態,並估測出對於此孕期重要的成長參數。然而,此孕期的超音波影像常因為胎兒的發育程度尚不完整,以及胎兒本身與子宮內膜的大量相連等問題,易造成醫師量測參數產生人為誤差以及相當大的變異;而一般胎兒超音波共通的缺陷如:訊號雜訊比太低、解剖構造不明顯等缺陷更是加大了量測上的難度。本論文為發展一套自動化三維超音波影像胎兒頭部與軀幹結構分割系統,提出一個以模型為基礎之分割方法,完成自動化的胎兒頭部與軀幹結構分割,來輔助醫師量測三項具臨床意義之外觀成長參數。
在本論文所提出以模型為基礎的分割方法,首先我們藉由實際影像資料分別建立出胎兒頭部與軀幹之統計性模型,再由專家定義所要量測的標記點。接著,我們利用特徵點建立出座標系,以進行模型與影像的對位,完成模型之位置與方向的初始化。然後我們利用Active Shape Model的限制讓模型形變至一個靠近影像中目標邊界的合理的形狀後,再設計一個根據模型附近的影像環境變化的幾何形狀限制與影像特性之能量函數,來補償模型因Active Shape Model限制而無法貼合到的部份目標邊緣。我們利用三維模型的形變,克服了上述複雜之超音波影像缺陷並完成胎兒頭部與軀幹分割,並且將自動化量測之結果與專家手動量測之結果進行比較,從實驗當中可得到令人滿意的結果 (影像分割平均誤差小於2個像素)。
英文摘要 Ultrasound image (US) has been widely used for the diagnosis in clinical of gynecology. As the US image is inexpensive, easy to use, non-invasive real time and without radiation hazards, it has become the most popular imaging modality in recent years. More specifically, doctors can observe the real-time posture of fetus and evaluate the fetal growth in the uterus by using 3D ultrasound images during prenatal care. However, due to the difficulties in treating highly-noise US images, the fully automatic image segmentation tool for the fetal ultrasound is still lack. Different to previous studies in US image segmentations for the second or the third trimester, we focus on the analysis of fetus image of the first trimester. In addition to the speckle noise and fuzzy boundaries, there are other important artifacts in the first-trimester fetal US image; such as the blurred image boundaries due to poor fetal development, or the weak edges due to the attachment of endometrium to fetus. Thus, we proposed a model-based segmentation method to automatically segment the fetal head and trunk structures, which assist doctors to measure the fetal parameters for clinical evaluations. First, we construct the statistical shape models of both fetal head and trunk by using the expert-adjusted mesh shapes from training volume images. Second, by using some anatomical feature points in target volume image, we define the local coordinate system to initially align the shape model and image data. Third, we apply the Active Shape Model (ASM) mechanism to adjust the shape model with a limited number of shape components. Thus, the global shape of the deformed model can mostly fit to the fetal boundaries. However, some local shape deviations still exist between the deformed shape and the actual image boundaries. Consequently, we design a 3D freeform deformation by using snake algorithm to improve the fitness between model and image. The experimental results show that the proposed method overcomes the difficulties and achieves good consistency between the automatic and manual measurements.
1.1 Motivation...1
1.2 Related Work...3
1.3 Thesis Organization and System Flow Chart...7
3.1 Initial manually-segmented mesh model construction...12
3.2 Adaptation of Training Shapes...15
3.2.1 Image Preprocessing...19
3.2.2 Anatomical Feature-based Registration...22
3.2.3 Position Correction of the prototype models...30
3.2.4 Model Scaling...33
3.2.5 3D Deformation with Manual Adjustment...35
3.3 Statistics of the training set...38
4.1 Active Shape Model Adaptation...43
4.2 Local freeform Deformations...47
4.2.1 Image feature classification according to the environments...49
4.2.2 3D Dynamic Deformation...52
5.1 Accuracy Evaluation of Segmentation Results...58
5.2 Clinical Parameters Measurement...61
5.3 The Result of Segmentation...64
5.3.1 Segmentation result of 2D planes...64
5.3.2 Appearance of 3D deformable model...67
6.1 Conclusion...71
6.2 Future Work...73
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