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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Updated: Oct 8, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Voxelisation Algorithms and Data Structures: A Review.

Mitko Aleksandrov1, Sisi Zlatanova1, David J Heslop2

  • 1The School of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary
This summary is machine-generated.

This review evaluates voxelization algorithms and data structures for 2D/3D objects. Surface voxelization is preferred for efficiency, while NanoVDB excels in dynamic scene handling.

Keywords:
algorithmsdata structuresgeometric primitivesvoxelvoxelisation

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Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Data Structures

Background:

  • Voxel-based representations are crucial for diverse 3D applications.
  • Existing methods require unified structures, multi-resolution capabilities, and flexible algorithms.
  • A comprehensive evaluation of voxelization techniques and data structures is needed.

Purpose of the Study:

  • To review common properties and algorithms for voxelizing 2D and 3D objects.
  • To evaluate various voxel data structures for static and dynamic applications.
  • To compare the efficiency and applicability of different voxelization approaches.

Main Methods:

  • Analysis of voxelization algorithms for geometric primitives (points, lines, triangles, surfaces, solids).
  • Categorization of line voxelization algorithms based on connectivity and curve representation.
  • Evaluation of static and dynamic voxel data structures, including SVO-based and NanoVDB.

Main Results:

  • Surface voxelization is generally more efficient and memory-sparing than solid voxelization.
  • Static grids are optimized for memory with methods like SVDAG and SSVDAG.
  • NanoVDB is identified as the leading dynamic voxel data structure for speed and large-scale scene management.

Conclusions:

  • This work provides a foundational review of voxelization algorithms and data structures.
  • The findings guide the selection of appropriate voxelization methods for specific 3D tasks.
  • Understanding these techniques is vital for advancing computer graphics and related fields.