Hepatic Vein Segmentation in CT Images using Fast Marching Method Driven by Gaussian Mixture Models
نویسنده
چکیده
Liver cancer is a serious disease in human beings. An effective way to cure liver cancer is the liver transplant operation. However, to make the surgical plan, the doctors need to know the structure, location and thickness of the hepatic vein. Therefore, hepatic vein segmentation is an initial and crucial step in liver cancer surgery. This thesis focuses on segmentation of hepatic veins from abdominal CT images. The purpose of this work is to obtain a volumetric hepatic vein model from the abdominal CT for liver transplant operation. To solve this problem, this thesis proposes a fast marching method driven by Gaussian mixture models (GMM) to segment hepatic vein from CT images. Anisotropic smoothing is applied to the original CT data to remove the noise. After that, GMMs are built for both hepatic vein area and non-hepatic vein areas based on hand-draw sampling points. The fast-marching propagation speed at each location is controlled by the generated GMMs. After that, a parametric cylinder model based algorithm is proposed to remove the unnecessary vena cava from the segmentation result. The segmentation results are analyzed and discussed.
منابع مشابه
IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کاملImage Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کاملPulmonary Arteries Segmentation and Feature Extraction through Slice Marching
We propose a novel method, slice marching 1, for segmenting opacified vessels tree in 3D images (volume data), from multislice computed tomography (MSCT) scans. The method uses fast marching with freezing of boundaries to advance inside the vessel, slice per slice. Large scale features, such as vessel section and curvature, are evaluated for each slice. These features can then be used to influe...
متن کاملA Hybrid 3D Colon Segmentation Method Using Modified Geometric Deformable Models
Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis. Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models...
متن کاملIntrathoracic Airway Tree Segmentation from CT Images Using a Fuzzy Connectivity Method
Introduction: Virtual bronchoscopy is a reliable and efficient diagnostic method for primary symptoms of lung cancer. The segmentation of airways from CT images is a critical step for numerous virtual bronchoscopy applications. Materials and Methods: To overcome the limitations of the fuzzy connectedness method, the proposed technique, called fuzzy connectivity - fuzzy C-mean (FC-FCM), utilized...
متن کامل