scholarly journals MARCHING CUBES IN AN UNSIGNED DISTANCE FIELD FOR SURFACE RECONSTRUCTION FROM UNORGANIZED POINT SETS

2010 ◽  
Keyword(s):  
Surface Reconstruction ◽  
Marching Cubes ◽  
Point Sets ◽  
Distance Field

CPU-GPU Rendering of CT Scan Images for Vertebra Reconstruction from CT Scan Images with a Calibration Policy

2020 ◽  
pp. 1-5
Author(s):  
Usman Khan ◽  
Usman Khan ◽  
AmanUllah Yasin ◽  
Imran Shafi ◽  
Muhammad Abid
Keyword(s):  
3D Reconstruction ◽  
Ct Scan ◽  
Surface Reconstruction ◽  
3D Visualization ◽  
Cervical Vertebra ◽  
Marching Cubes ◽  
Affine Transform ◽  
Visualization Techniques ◽  
Visualization Algorithms ◽  
Gpu Implementation

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.

Medical Image Segmentation and Reconstruction Based on Bayesian Level Set Method and Marching Cubes Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 4832-4836
Author(s):  
Yao Tien Chen
Keyword(s):  
Level Set Method ◽  
Surface Reconstruction ◽  
Level Set ◽  
Statistical Tests ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Marching Cubes ◽  
Appropriate Treatment ◽  
Marching Cubes Algorithm

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.

A flexible framework for surface reconstruction from large point sets

2007 ◽  
Vol 31 (2) ◽  
pp. 190-204 ◽  
Author(s):  
Rémi Allègre ◽  
Raphaëlle Chaine ◽  
Samir Akkouche
Keyword(s):  
Surface Reconstruction ◽  
Point Sets ◽  
Flexible Framework

scholarly journals Feature-Preserving Surface Reconstruction and Simplification from Defect-Laden Point Sets

2013 ◽  
Vol 48 (2) ◽  
pp. 369-382 ◽  
Author(s):  
Julie Digne ◽  
David Cohen-Steiner ◽  
Pierre Alliez ◽  
Fernando de Goes ◽  
Mathieu Desbrun
Keyword(s):  
Surface Reconstruction ◽  
Point Sets ◽  
Feature Preserving

scholarly journals A parallel dual marching cubes approach to quad only surface reconstruction

2021 ◽  
Author(s):  
Roberto Grosso ◽  
Daniel Zint
Keyword(s):  
Data Structure ◽  
Surface Reconstruction ◽  
Synthetic Data ◽  
Data Sets ◽  
Marching Cubes ◽  
Volume Data ◽  
Almost Everywhere ◽  
Reconstruction Methods ◽  
Novel Method ◽  
Reconstructed Surface

AbstractWe present a novel method that reconstructs surfaces from volume data using a dual marching cubes approach without lookup tables. The method generates quad only meshes which are consistent across cell borders, i.e., they are manifold and watertight. Vertices are positioned exactly on the reconstructed surface almost everywhere, leading to higher accuracy than other reconstruction methods. A halfedge data structure is used for storing the meshes which is convenient for further processing. The method processes elements in parallel and therefore runs efficiently on GPU. Due to the transition between layers in volume data, meshes have numerous vertices with valence three. We use simplification patterns for eliminating quads containing these vertices wherever possible which reduces the number of elements and increases quality. We briefly describe a CUDA implementation of our method, which allows processing huge amounts of data on GPU at almost interactive time rates. Finally, we present runtime and quality results of our method on medical and synthetic data sets.

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