Abstract
This paper presents a coarse-grain approach for segmentation of objects with gray levels appearing in volume data. The input data is on a 3D structured grid of vertices v(i, j, k), each associated with a scalar value. In this paper, we consider a voxel as a κ × κ × κ cube and each voxel is assigned two values: expectancy and standard deviation (E-SD). We use the Weibull noise index to estimate the noise in a voxel and to obtain more precise E-SD values for each voxel. We plot the frequency of voxels which have the same E-SD, then 3D segmentation based on the Weibull E-SD field is presented. Our test bed includes synthetic data as well as real volume data from a confocal laser scanning microscope (CLSM). Analysis of these data all show distinct and defining regions in their E-SD fields. Under the guide of the E-SD field, we can efficiently segment the objects embedded in real and simulated 3D data.
Original language | English (US) |
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Pages (from-to) | 320-328 |
Number of pages | 9 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Volume | 9 |
Issue number | 3 |
DOIs | |
State | Published - Jul 2003 |
Keywords
- 3D segmentation
- CLSM
- Confocal laser scanning microscope
- Noise index
- Weibull E-SD field
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design