A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images
We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriat...
| Main Authors: | , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
Hindawi Publishing Corporation
2014
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3988742/ |
| id |
pubmed-3988742 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-39887422014-05-06 A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images Wang, Yin Jiang, Han Research Article We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy. Hindawi Publishing Corporation 2014 2014-04-01 /pmc/articles/PMC3988742/ /pubmed/24803950 http://dx.doi.org/10.1155/2014/302805 Text en Copyright © 2014 Y. Wang and H. Jiang. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Wang, Yin Jiang, Han |
| spellingShingle |
Wang, Yin Jiang, Han A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images |
| author_facet |
Wang, Yin Jiang, Han |
| author_sort |
Wang, Yin |
| title |
A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images |
| title_short |
A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images |
| title_full |
A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images |
| title_fullStr |
A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images |
| title_full_unstemmed |
A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images |
| title_sort |
nonparametric shape prior constrained active contour model for segmentation of coronaries in cta images |
| description |
We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3988742/ |
| _version_ |
1612078811831074816 |