Vessel Laminae Segmentation
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Accurate and rapid segmentation of microvasculature from three-dimensional (3-D) images is important for diverse studies in neuroscience, tumor biology, stem-cell niches, cancer stem cell niches, and other areas. Vessel segmentation algorithm presents a robust 3-D algorithm to segment vasculature that is image by labeling laminae, rather than lumenal volume. The signal is weak, sparse, noisy, non-uniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding & Hessian filtering based methods are not effective. | Accurate and rapid segmentation of microvasculature from three-dimensional (3-D) images is important for diverse studies in neuroscience, tumor biology, stem-cell niches, cancer stem cell niches, and other areas. Vessel segmentation algorithm presents a robust 3-D algorithm to segment vasculature that is image by labeling laminae, rather than lumenal volume. The signal is weak, sparse, noisy, non-uniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding & Hessian filtering based methods are not effective. | ||
| − | [[Image:Sample_vessel_image.jpg|right|thumb|300px|A 2-D projection of a blood vessel image]] | + | [[Image:Sample_vessel_image.jpg|right|thumb|300px|A 2-D projection of a 3-D blood vessel image. Original res: 0.36 x 0.36 x 1.5 µm<sup>3</sup>. Original stack size: 1024x1024x77 voxels<sup>3</sup>]] |
==The Algorithm== | ==The Algorithm== | ||
Revision as of 17:50, 10 April 2009
Contents |
Vessel Segmentation
Background and motivation
Accurate and rapid segmentation of microvasculature from three-dimensional (3-D) images is important for diverse studies in neuroscience, tumor biology, stem-cell niches, cancer stem cell niches, and other areas. Vessel segmentation algorithm presents a robust 3-D algorithm to segment vasculature that is image by labeling laminae, rather than lumenal volume. The signal is weak, sparse, noisy, non-uniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding & Hessian filtering based methods are not effective.
