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A parallelized surface extraction algorithm for large binary image data sets based on an adaptive 3D delaunay

Yingliang Ma¹, Kurt Saetzler

¹Division of Imaging Sciences, King's College London, Guy's Hospital, London, UK. y.ma@kcl.ac.uk

IEEE Transactions on Visualization and Computer Graphics

|November 13, 2007

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This study introduces a new 3D surface extraction method for binary images. It generates detailed surface meshes with fewer triangles than marching cubes, reducing noise and staircase effects for better biomedical imaging.

Area of Science:

Computer Vision
Medical Imaging
Scientific Visualization

Background:

Voxel-based algorithms like Marching Cubes (MC) are common for 3D surface extraction from image data.
MC can produce excessive triangles and exhibit staircase artifacts, especially with noisy or anisotropic data.
Efficient and accurate surface extraction is crucial for analyzing complex structures in biomedical imaging.

Purpose of the Study:

To present a novel 3D subdivision strategy for extracting surfaces from binary image data.
To develop an iterative approach generating multi-level detail surface meshes.
To improve upon existing methods by reducing triangle count and eliminating artifacts.

Main Methods:

An iterative 3D subdivision strategy is employed to generate surface meshes.

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The algorithm refines surfaces to capture varying levels of detail.

Parallelization is achieved by subdividing image space into blocks for multi-threaded processing.

Main Results:

The novel approach generates surface meshes with approximately 10% of the triangles compared to the Marching Cubes algorithm.
The "staircase effect" commonly seen in voxel-based methods is eliminated, even with non-uniformly sampled images.
The algorithm demonstrates robust performance against image noise and scales well for large datasets.

Conclusions:

The proposed 3D subdivision strategy offers a more efficient and robust alternative for surface extraction from binary image data.
The method's ability to reduce triangle count, eliminate artifacts, and handle noise makes it highly suitable for biomedical applications.
Parallelization capabilities ensure suitability for processing large-scale medical image datasets.