Name
Abstract | ||
|---|---|---|
Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the patient image by an affine and an intensity-based non rigid registration. Based on this rough initialisation, the level set functions corresponding to particular objects of interest of the deformed atlas are used to segment the corresponding objects in the patient image. We propose a technique to derive a dense deformation field from the motion of these level set functions. This is particularly important when we want to infer the position of invisible structures like the brain sub-thalamic nuclei from the position of visible surrounding structures. This can also be advantageously exploited to register an atlas following a hierarchical approach. Results are shown on 2D synthetic images and 2D real images extracted from brain and prostate MR volumes and neck CT volumes. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICIP.2005.1530298 | Image Processing, 2005. ICIP 2005. IEEE International Conference |
Keywords | Field | DocType |
biomedical MRI,computerised tomography,image registration,image segmentation,medical image processing,rough set theory,2D real images,2D synthetic images,atlas-based segmentation,brain subthalamic nuclei,dense deformation field,global registration techniques,intensity-based nonrigid registration,level set functions,level set tracking,local registration,medical image segmentation,medical images,patient image | Computer vision,Scale-space segmentation,Pattern recognition,Image texture,Computer science,Segmentation,Level set,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Real image,Image registration | Conference |
Volume | ISSN | ISBN |
2 | 1522-4880 | 0-7803-9134-9 |
Citations | PageRank | References |
9 | 0.66 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valerie Duay | 1 | 129 | 9.53 |
| Nawal Houhou | 2 | 39 | 3.57 |
| Jean-Philippe Thiran | 3 | 2320 | 257.56 |