| Title | Segmentation of ultrasound images of the carotid using RANSAC and cubic splines |
| Publication Type | Journal Article |
| 2011 |
| Authors | Rocha, R, Campilho, A, Silva, J, Azevedo, E, Santos, R |
| Journal | Computer Methods and Programs in BiomedicineComput. Methods Programs Biomed. |
| Volume | 101 |
| Issue | 1 |
| Pagination | 94 - 106 |
| Date Published | 2011/// |
| 01692607 (ISSN) |
| Algorithms, article, B scan, B-mode images, Carotid, carotid artery, Carotid Artery Diseases, Carotid Artery, Common, Class II, clinical article, Common carotid artery, Cubic spline, Detection error, echography, Edge map, Female, Gain function, Geometric models, Geometry, human, Humans, Image Interpretation, Computer-Assisted, image processing, Image segmentation, Image sets, Intensity gradients, Longitudinal section, male, Manual segmentation, mathematical model, Medical experts, Medical imaging, Non-linear smoothing, Pattern Recognition, Automated, quantitative analysis, Quantitative evaluation, random sample, Random sample consensus, RANSAC, Semi-automatic segmentation, Splines, Ultrasonic applications, Ultrasonics, Ultrasonography, Ultrasound image, Ultrasound images |
| A new algorithm is proposed for the semi-automatic segmentation of the near-end and the far-end adventitia boundary of the common carotid artery in ultrasound images. It uses the random sample consensus method to estimate the most significant cubic splines fitting the edge map of a longitudinal section. The consensus of the geometric model (a spline) is evaluated through a new gain function, which integrates the responses to different discriminating features of the carotid boundary: the proximity of the geometric model to any edge or to valley shaped edges; the consistency between the orientation of the normal to the geometric model and the intensity gradient; and the distance to a rough estimate of the lumen boundary.A set of 50 longitudinal B-mode images of the common carotid and their manual segmentations performed by two medical experts were used to assess the performance of the method. The image set was taken from 25 different subjects, most of them having plaques of different classes (class II to class IV), sizes and shapes.The quantitative evaluation showed promising results, having detection errors similar to the ones observed in manual segmentations for 95% of the far-end boundaries and 73% of the near-end boundaries. © 2011 Elsevier Ireland Ltd. |
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