Name
Abstract | ||
|---|---|---|
This study presents an approach to the segmentation of the right ventricle (RV) from a sequence of cardiac magnetic resonance (MR) images. Automatic delineation of the RV is difficult because of its complex morphology, thin and ill-defined borders, and the photometric similarities between the connected cardiac regions such as papillary muscles and heart wall. Further, geometric/photometric models are hard to build from a finite training set because of the significant differences in size, shape, and intensity between subjects. In this study, we propose to use a non-rigid registration method developed recently to obtain the point correspondence in a sequence of cine MR images. Given the segmentation on the first frame, the proposed method segments both endocardial and epicardial borders of the RV using the obtained point correspondence, and relaxes the need of a training set. The proposed method is evaluated quantitatively on common data set by comparison with manual segmentation, which demonstrated competitive results in comparison with recent methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610424 | EMBC |
Keywords | Field | DocType |
nonrigid registration method,endocardial borders,geometric-photometric models,heart wall,cardiology,point correspondence,epicardial borders,mri,image segmentation,papillary muscles,connected cardiac regions,cardiac right ventricular segmentation,automatic delineation,biomedical mri,photometric similarities,image sequences,complex morphology,image registration,muscle,cardiac magnetic resonance image sequence,medical image processing,finite training set,shape,heart | Training set,Computer vision,Point correspondence,Scale-space segmentation,Computer science,Segmentation,Cardiac magnetic resonance,Image segmentation,Ventricle,Artificial intelligence,Image registration | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 1 |
PageRank | References | Authors |
0.36 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Punithakumar | 1 | 14 | 2.28 |
| Michelle Noga | 2 | 1 | 0.70 |
| Pierre Boulanger | 3 | 70 | 8.24 |