3D Visualization and Reconstruction of Lung Cancer Images using Marching Cubes Algorithm

Author(s):  
Khairani Arlinda Mandaliana ◽  
Tri Harsono ◽  
Riyanto Sigit
2020 ◽  
pp. 1-5
Author(s):  
Usman Khan ◽  
Usman Khan ◽  
AmanUllah Yasin ◽  
Imran Shafi ◽  
Muhammad Abid

In this work GPU implementation of classic 3D visualization algorithms namely Marching Cubes and Raycasting has been carried for cervical vertebra using VTK libraries. A proposed framework has been introduced for efficient and duly calibrated 3D reconstruction using Dicom Affine transform and Python Mayavi framework to address the limitation of benchmark visualization techniques i.e. lack of calibration, surface reconstruction artifacts and latency.


2021 ◽  
Vol 47 (3) ◽  
pp. 215-223
Author(s):  
Delia Irazú Hernández Farías ◽  
Rafael Guzmán Cabrera ◽  
Teodoro Cordova Fraga ◽  
José Zacarías Huamaní Luna ◽  
Jose Francisco Gomez Aguilar

2005 ◽  
Vol 5 (2) ◽  
pp. 111-115 ◽  
Author(s):  
Tomoyuki Fujimori ◽  
Hiromasa Suzuki ◽  
Yohei Kobayashi ◽  
Kiwamu Kase

This paper describes a new algorithm for contouring a medial surface from CT (computed tomography) data of a thin-plate structure. Thin-plate structures are common in mechanical structures, such as car body shells. When designing thin-plate structures in CAD (computer-aided design) and CAE (computer-aided engineering) systems, their shapes are usually represented as surface models associated with their thickness values. In this research, we are aiming at extracting medial surface models of thin-plate structures from their CT data for use in CAD and CAE systems. Commonly used isosurfacing methods, such as marching cubes, are not applicable to contour the medial surface. Therefore, we first extract medial cells (cubes comprising eight neighboring voxels) from the CT data using a skeletonization method to apply the marching cubes algorithm for extracting the medial surface. It is not, however, guaranteed that the marching cubes algorithm can contour those medial cells (in short, not “marching cubeable”). In this study, therefore we developed cell operations that correct topological connectivity to guarantee such marching cubeability. We then use this method to assign virtual signs to the voxels to apply the marching cubes algorithm to generate triangular meshes of a medial surface and map the thicknesses of thin-plate structures to the triangle meshes as textures. A prototype system was developed to verify some experimental results.


2020 ◽  
Vol 40 (2) ◽  
pp. 8-15 ◽  
Author(s):  
William E. Lorensen ◽  
Chris Johnson ◽  
Dave Kasik ◽  
Mary C. Whitton

2011 ◽  
Vol 110-116 ◽  
pp. 4832-4836
Author(s):  
Yao Tien Chen

We propose an approach, integrating Bayesian level set method with modified marching cubes algorithm for brain tissue and tumor segmentation and surface reconstruction. First, we extend the level set method based on the Bayesian risk to three-dimensional segmentation. Then, the three-dimensional Bayesian level set method is used to segment solid three-dimensional targets (e.g., tissue, whole brain, or tumor) from serial slice of medical images. Finally, the modified marching cubes algorithm is used to continuously reconstruct the surface of targets. Since each step can definitely obtain an appropriate treatment by statistical tests, the tissue and tumor segmentation and surface reconstruction are expected to be satisfied.


Sign in / Sign up

Export Citation Format

Share Document