Name
Abstract | ||
|---|---|---|
Non-rigid image registration of large 3D volumes depends heavily on image size. Voxel-based approaches for registration of large data sets are computationally intensive and take a lot of processing time, usually exponentially proportional to their resolution. In this paper, we present a method for non-rigid image registration that is independent of the image size. It can be applied to large data sets without sacrificing performance. The method is based on computing a thin plate spline (TPS) transformation between corresponding control points derived from a rapid and highly accurate model-based segmentation. The segmentation process does not depend on the image size and defines accurate point-based correspondence. Those properties were used to estimate an approximating TPS between a source and a target volumes and to deform the target to align with the source. The method was applied to image sequences of pediatric 3D ultrasound data. Registration of 3D ultrasound images is challenging because of poor-defined image gradients, artifacts, low contrast, noise and speckle. Quantitative and qualitative results indicate that the method is capable of registering large volumes acquired throughout the cardiac cycle from both intra- and inter-patient ultra- sound sequences. More importantly, the actual registration does not depend on image intensity and can be applied to multi-modal applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPRW.2014.132 | Computer Vision and Pattern Recognition Workshops |
Keywords | Field | DocType |
image registration,image segmentation,image sequences,medical image processing,paediatrics,splines (mathematics),ultrasonic imaging,3D ultrasound images,TPS transformation,image artifacts,image gradients,image noise,image sequences,image speckle,inter-patient ultrasound sequences,intra-patient ultrasound sequences,low contrast image,model-based segmentation,nonrigid image registration,pediatric 3D ultrasound data,point-based correspondence,thin plate spline | Computer vision,Scale-space segmentation,Pattern recognition,Feature detection (computer vision),Computer science,Range segmentation,Image texture,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Image registration | Conference |
ISSN | Citations | PageRank |
2160-7508 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Babak Matinfar | 1 | 0 | 0.34 |
| Lyubomir Zagorchev | 2 | 66 | 7.98 |