A Level-Set Based Method for Vessel Navigation

2011 ◽  
Vol 474-476 ◽  
pp. 1345-1350
Author(s):  
Xin Rong Lv
Keyword(s):  
Blood Vessel ◽  
Level Set Method ◽  
Level Set ◽  
Graphics Processing Unit ◽  
Initial Contour ◽  
Processing Unit ◽  
3D Texture ◽  
Analysis System ◽  
Vessel Extraction ◽  
Fast Extraction

Automatic blood vessel analysis plays an important role in computer-aided diagnosis of the vessels. In the blood vessel analysis system, extraction and navigation of vessels are two crucial components, and fast extraction and automatic navigation algorithms attract more and more attention. In this paper, to accelerate the extraction of blood vessel, an initial contour algorithm is proposed to produce an initial contour for level-set method. Then, the level-set method is introduced to extract the vessel more precisely. Finally, the navigation of the extracted blood vessel is realized based on a 3D texture volume rendering algorithm based on graphics processing unit (GPU). The experimental results illustrate the effectiveness of the proposed vessel extraction and navigation scheme.

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.

scholarly journals Combined Acceleration Methods for Solid Rocket Motor Grain Burnback Simulation Based on the Level Set Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ran Wei ◽  
Futing Bao ◽  
Yang Liu ◽  
Weihua Hui
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Rocket Motor ◽  
Processing Unit ◽  
Solid Rocket Motor ◽  
Distance Field ◽  
Acceleration Methods ◽  
Generating Method ◽  
Simulation Based ◽  
Solid Rocket

A detailed study of a set of combined acceleration methods is presented with the objective of accelerating the solid rocket motor grain burnback simulation based on the level set method. Relevant methods were improved by making use of unique characteristics of the grains, and graphical processing unit (GPU) parallelization is utilized to perform the computationally intensive operations. The presented flow traced the expansion of burning surfaces, and then Boolean operations were applied on the resulting surfaces to extract various geometric metrics. The initial signed distance field was built by an improved distance field generating method, and a highly optimized GPU kernel was used for estimating the gradient required by the level set method. An innovative Boolean operation method, thousands of times faster than ordinary ones, was ultimately proposed. Performance tests show that the overall speedup was close to 15 on desktop-class hardware, simulation results were proven to converge to analytical results, and the error boundary was 0.25%.

Sign in / Sign up

Export Citation Format

Share Document