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研究生(外文):Yu-Tsan Kuo
論文名稱(外文):A Liver Vessel Segmentation and Surface Reconstruction System from CT Images Used for Preoperative Simulation
指導教授(外文):Chin-Shyurng Fahn
口試委員(外文):Hui-Wen FengJung-Tang HuangJung-Hua Wang
外文關鍵詞:CT imagevessel surface reconstructionGraph CutMarching Cubeimage denoiseAdaptive Thresholdpreoperative simulation
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在醫院的手術治療前,醫師通常只能透過2D的電腦斷層掃描(Computerized tomography, CT)影像以及個人自身的經驗,來診斷病人的病症位置及狀況,在各式器官手術中,以肝臟最為困難,因為肝臟的血管為人體結構最複雜的部份,一般器官只分為動脈與靜脈,而在肝臟中,還有一組血管稱作肝門靜脈,由於肝臟血管的複雜度極高,所以在手術過程中很容易有不慎切到大型血管的情況,這時會造成手術過程混亂,且流出來的血液很容易蓋住病徵,不僅會使病人陷入危險之中,還會導致手術時間過長。目前醫療影像的拍攝技術仍無法直接輸出完整的立體模型,病人往往無法從影像中想像出自己血管的位置,而醫師也只能憑藉著自身的經驗來判斷大約的位置,這樣很容易造成醫師與病人之間的溝通障礙。
基於上述理由,本論文開發了一套的系統重建出肝臟血管的外型,可以提供醫師做手術前的模擬與講解使用。在影像處理的階段,我們首先利用圖像分割演算法(Graph Cut Algorithm)從斷層掃描影像中,取得肝臟的位置,再利用非局部平均的演算法(Non-Local Means Algorithm)對整張圖片的大範圍進行降噪,最後採取非等向性擴散濾波器(Anisotropic Diffuse Filter)將影像中過度細微的血管去除。在表面重建的階段,我們先利用經過影像處理完的影像,使用傳統取閥值的方式、自適應閥值(Adaptive Threshold),以及影像二值化來取出血管的部分,再利用電腦斷層掃瞄影像的特性,將平面影像轉變成立體的點雲,最後再利用行進立方體演算法(Marching Cube Algorithm)重建出血管模型,並採用移動最小二乘法(Moving Least Square)的方式平滑血管模型的表面。
Because the structure of liver vessel is the most complicated in anatomy, the surgery on liver is the most difficult medical treatment in hospital. Before surgery in hospital, doctors can only use their personal experience and Computerized Tomography (CT) images to diagnose patient’s disease. There are only two kinds of vessel in general organs, one is artery and another is vein. However, there are three kinds of vessel in liver, which comprise artery, vein, and portal vein. Owing to the complexity of liver vessel, it is possible to accidently cut a large vessel during surgery. If doctors cut the big vessel in surgical procedure, it makes the patient under the risk because the blood flows out quickly and covers the entire organ, and increases the difficulty and time in surgery. The current technique of CT cannot output the model of vessel. Therefore, patients cannot know the actual location of their disease, and doctors can only use their personal experience to find the approximate position, which causes the communication barrier between patients and doctors.
According to the reasons mentioned above, we develop a system of reconstructing liver vessel surface, which is used for preoperative simulation. There are two phases in this system, one is image preprocessing, and another is surface reconstruction. In image preprocessing, we use Graph Cut Algorithm to cut the area of liver first. Next, we adopt Non-Local Means Algorithm to reduce noise in the entire image. The last step in image preprocessing, we use Anisotropic Diffuse filter to remove the vessel which is too small. In surface reconstruction, we use an adaptive threshold method to filter liver vessel in CT images, then we use the characteristic in CT to create the point cloud. Finally, we use Marching Cube algorithm to reconstruct liver vessel surface and Moving Least Square to smooth the liver vessel model.
List of Figures
List of Tables
Chapter 1 Introduction
1.1 Overview
1.2 Motivation
1.3 System Description
1.4 Thesis organization
Chapter 2 Related Work
2.1 Introduction to Computerized Tomography
2.2 Liver Vessel Segmentation
2.3 Surface Reconstruction
Chapter 3 Image Preprocessing
3.1 Graph Cut
3.2 Denoising
3.3 Anisotropic Diffuse Filter
Chapter 4 Surface Reconstruction
4.1 Threshold method and Binarization
4.2 Create Point Cloud
4.3 Reconstruct from Point Cloud
Chapter 5 Experimental Results and Discussion
5.1 Dataset
5.2 Image Preprocessing
5.3 Surface Reconstruction
Chapter 6 Conclusions and Future Work
6.1 Conclusions
6.2 Future Work
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