Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images
Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of at...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2012/439597 |
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| author | Hassen Lazrag Med Saber Naceur |
| author_facet | Hassen Lazrag Med Saber Naceur |
| author_sort | Hassen Lazrag |
| collection | DOAJ |
| description | Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect lumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourlet multiresolution analysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands. The circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours. The anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the curve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via 20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the relative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional level-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries. |
| format | Article |
| id | doaj-art-c912fd3dfed142ea920b6ba39ffdc1df |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-c912fd3dfed142ea920b6ba39ffdc1df2025-08-20T02:19:41ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962012-01-01201210.1155/2012/439597439597Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS ImagesHassen Lazrag0Med Saber Naceur1Ecole Nationale d’Ingénieurs de Tunis (ENIT), B.P. 37, Le Belvedere, 1002 Tunis, TunisiaEcole Nationale d’Ingénieurs de Tunis (ENIT), B.P. 37, Le Belvedere, 1002 Tunis, TunisiaIntravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect lumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourlet multiresolution analysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands. The circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours. The anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the curve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via 20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the relative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional level-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries.http://dx.doi.org/10.1155/2012/439597 |
| spellingShingle | Hassen Lazrag Med Saber Naceur Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images International Journal of Biomedical Imaging |
| title | Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images |
| title_full | Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images |
| title_fullStr | Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images |
| title_full_unstemmed | Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images |
| title_short | Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images |
| title_sort | combination of the level set methods with the contourlet transform for the segmentation of the ivus images |
| url | http://dx.doi.org/10.1155/2012/439597 |
| work_keys_str_mv | AT hassenlazrag combinationofthelevelsetmethodswiththecontourlettransformforthesegmentationoftheivusimages AT medsabernaceur combinationofthelevelsetmethodswiththecontourlettransformforthesegmentationoftheivusimages |