Competing Front for Directed Surface Extraction from CT Images

2012 ◽  
Vol 195-196 ◽  
pp. 534-538
Author(s):  
Xue Shu Liu
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Ct Images ◽  
Experimental Results ◽  
Second Step ◽  
Simple Method ◽  
Zero Level ◽  
Interior Surface ◽  
Surface Extraction ◽  
Speed Function

Outward directed surface extraction from imaging modalities is the first task in the design of implants. In this paper a method based on level set method is proposed to extract the directed surface from CT images. The process is composed of two steps. In the first step, Level Set method with a new speed function is employed to evolve zero level set to its destination and used to cut the desired bone part from the input CT images. In the second step, a simple method is used to extract the directed surface, usually the outward surface, from the separated bone part by removing the interior surface. The experimental results show the proposed method works well.

NEIGHBOR SUPPORT BASED SPEED FUNCTION FOR LEVEL SET METHOD

2010 ◽  
Vol 18 (spec01) ◽  
pp. 149-158 ◽  
Author(s):  
XUESHU LIU ◽  
YUTAKA OHTAKE ◽  
HIROMASA SUZUKI
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Normal Direction ◽  
Mesh Segmentation ◽  
Zero Level ◽  
Sharp Feature ◽  
Normal Diffusion ◽  
Speed Function ◽  
Made In

With a different speed function, Level Set Method has been widely applied to many applications. Generally speaking, speed function may depend on many factors, such as curvature, normal direction. In this paper, we discuss a novel speed function which is only determined by neighbors' support. With enough support, zero level set can move or stop. Otherwise, it must wait for a moment before a decision to move or stop is made. In addition, an algorithm based on normal diffusion is proposed to smooth the zero level set, which can preserve the sharp feature and round corner at the same time. Experimentally, the proposed method has been successfully used for interested objection segmentation and mesh segmentation.

A Novel Multiphase Level Set Method to Image Segmentation Combined with Edge-Link Method

2013 ◽  
Vol 850-851 ◽  
pp. 839-843
Author(s):  
Hong Jiang Wu ◽  
Hai Yan Zhao ◽  
Jin Meng ◽  
Jing Zhao
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Human Motion ◽  
Experimental Results ◽  
Zero Level ◽  
Level Set Segmentation ◽  
Initial Zero

An improved level set PDE based on the Chan-Vese multiphase level set method is proposed. Because the segmentation results is well influenced by the initial zero level set, we use the method based on Edge-Link to obtain initial zero level set of the proposed multiphase level set segmentation model. Experimental results of ambiguous edges in human motion image suggest the efficiency and accuracy of the algorithm in its segmentation operations.

scholarly journals A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Hiroshi Fujita
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Negative Impact ◽  
Level Set Methods ◽  
Region Growing ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Ct Image ◽  
Fast Marching

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

A Parametric Level Set Method for Topology Optimization based on Deep Neural Network (DNN)

2021 ◽  
pp. 1-14
Author(s):  
Hao Deng ◽  
Albert C. To
Keyword(s):  
Neural Network ◽  
Topology Optimization ◽  
Differential Equations ◽  
Level Set Method ◽  
Level Set ◽  
Deep Neural Network ◽  
Implicit Function ◽  
Zero Level ◽  
Fully Connected ◽  
Parametric Level Set Method

Abstract This paper proposes a new parametric level set method for topology optimization based on Deep Neural Network (DNN). In this method, the fully connected deep neural network is incorporated into the conventional level set methods to construct an effective approach for structural topology optimization. The implicit function of level set is described by fully connected deep neural networks. A DNN-based level set optimization method is proposed, where the Hamilton-Jacobi partial differential equations (PDEs) are transformed into parametrized ordinary differential equations (ODEs). The zero-level set of implicit function is updated through updating the weights and biases of networks. The parametrized reinitialization is applied periodically to prevent the implicit function from being too steep or too flat in the vicinity of its zero-level set. The proposed method is implemented in the framework of minimum compliance, which is a well-known benchmark for topology optimization. In practice, designers desire to have multiple design options, where they can choose a better conceptual design base on their design experience. One of the major advantages of DNN-based level set method is capable to generate diverse and competitive designs with different network architectures. Several numerical examples are presented to verify the effectiveness of proposed DNN-based level set method.

scholarly journals Adaptive Regularized Level Set Method for Weak Boundary Object Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Meng Li ◽  
Chuanjiang He ◽  
Yi Zhan
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Active Contours ◽  
Boundary Object ◽  
Level Curve ◽  
External Energy ◽  
Zero Level ◽  
Level Set Function ◽  
Wide Range ◽  
Level Set Evolution

An adaptive regularized level set method for image segmentation is proposed. A weightedp(x)-Dirichlet integral is presented as a geometric regularization on zero level curve, which is used to diminish the influence of image noise on level set evolution while ensuring the active contours not to pass through weak object boundaries. The idea behind the new energy integral is that the amount of regularization on the zero level curve can be adjusted automatically by the variable exponentp(x)to fit the image data. This energy is then incorporated into a level set formulation with an external energy term that drives the motion of the zero level set toward the desired objects boundaries, and a level set function regularization term that is necessary for maintaining stable level set evolution. The proposed model has been applied to a wide range of both real and synthetic images with promising results.

Size-adaptive hepatocellular carcinoma detection from 3D CT images based on the level set method

2012 ◽  
Author(s):  
Shuntaro Yui ◽  
Junichi Miyakoshi ◽  
Kazuki Matsuzaki ◽  
Toshiyuki Irie ◽  
Ryo Kurazume
Keyword(s):  
Hepatocellular Carcinoma ◽  
Level Set Method ◽  
Level Set ◽  
Ct Images ◽  
3D Ct

TRACKING MULTIPLE MOVING OBJECTS USING A LEVEL-SET METHOD

2004 ◽  
Vol 18 (02) ◽  
pp. 101-125 ◽  
Author(s):  
CHIA-JUNG CHANG ◽  
JUN-WEI HSIEH ◽  
YUNG-SHENG CHEN ◽  
WEN-FONG HU
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Moving Objects ◽  
Video Content ◽  
Subtraction Technique ◽  
Object Boundary ◽  
Novel Approach ◽  
Compensation Technique ◽  
Speed Function ◽  
Edge Based

This paper presents a novel approach to track multiple moving objects using the level-set method. The proposed method can track different objects no matter if they are rigid, nonrigid, merged, split, with shadows, or without shadows. At the first stage, the paper proposes an edge-based camera compensation technique for dealing with the problem of object tracking when the background is not static. Then, after camera compensation, different moving pixels can be easily extracted through a subtraction technique. Thus, a speed function with three ingredients, i.e. pixel motions, object variances and background variances, can be accordingly defined for guiding the process of object boundary detection. According to the defined speed function, different object boundaries can be efficiently detected and tracked by a curve evolution technique, i.e. the level-set-based method. Once desired objects have been extracted, in order to further understand the video content, this paper takes advantage of a relation table to identify and observe different behaviors of tracked objects. However, the above analysis sometimes fails due to the existence of shadows. To avoid this problem, this paper adopts a technique of Gaussian shadow modeling to remove all unwanted shadows. Experimental results show that the proposed method is much more robust and powerful than other traditional methods.

Optimization of Level Set Segmentation with Nonlinear Coherent Diffusion

2013 ◽  
Vol 787 ◽  
pp. 896-901
Author(s):  
Yong Hui Gao ◽  
Sheng Zheng Wang ◽  
Jie Yang
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Experimental Results ◽  
Segmentation Result ◽  
Gaussian Filter ◽  
Robust Method ◽  
Level Set Segmentation ◽  
Fine Structures ◽  
Spatially Varying

Level set method is convenient in image segmentation for the stabilization and veracity. Gaussian filter is usually taken as a preprocess to reduce the influence of weak edges due to noises, but the disadvantage is obvious: blur fine structures specially the important boundaries and lead to inaccurate segmentation result. This paper introduces a robust method which filters the images with a Nonlinear Coherent Diffusion (NCD) to accelerate the evolution of level set in a spatially varying manner. Experimental results show the performance of the proposed method in improving precision of segmentation.

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