Name
Title | ||
|---|---|---|
Application-driven MRI: joint reconstruction and segmentation from undersampled MRI data. |
Abstract | ||
|---|---|---|
Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10404-1_14 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Discrete cosine transform,Segmentation-based object categorization,Image segmentation,Reconstruction algorithm,Artificial intelligence,Discriminative model,Mixture model | Conference | 8673 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 5 |
PageRank | References | Authors |
0.51 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose Caballero | 1 | 663 | 22.59 |
| Wenjia Bai | 2 | 445 | 35.84 |
| Anthony N Price | 3 | 253 | 15.32 |
| Daniel Rueckert | 4 | 9338 | 637.58 |
| Jo Hajnal | 5 | 1796 | 119.03 |