Name
Abstract | ||
|---|---|---|
Radiological imaging of the prostate is becoming more popular among researchers and clinicians in searching for diseases, primarily cancer. Scans might be acquired with different equipment or at different times for prognosis monitoring, with patient movement between scans, resulting in multiple datasets that need to be registered. For these cases, we introduce a method for volumetric registration using curvature flow. Multiple prostate datasets are mapped to canonical solid spheres, which are in turn aligned and registered through the use of identified landmarks on or within the gland. Theoretical proof and experimental results show that our method produces homeomorphisms with feature constraints. We provide thorough validation of our method by registering prostate scans of the same patient in different orientations, from different days and using different modes of MRI. Our method also provides the foundation for a general group-wise registration using a standard reference, defined on the complex plane, for any input. In the present context, this can be used for registering as many scans as needed for a single patient or different patients on the basis of age, weight or even malignant and non-malignant attributes to study the differences in general population. Though we present this technique with a specific application to the prostate, it is generally applicable for volumetric registration problems. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Graphics | Computer vision,Population,Curvature,Computer science,Artificial intelligence,Prostate |
DocType | Volume | Citations |
Journal | abs/1608.00921 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saad Nadeem | 1 | 1 | 1.03 |
| Rui Shi | 2 | 298 | 28.07 |
| Joseph Marino | 3 | 70 | 11.35 |
| Wei Zeng | 4 | 75 | 3.80 |
| Xianfeng Gu | 5 | 0 | 0.68 |
| Arie Kaufman | 6 | 4154 | 453.50 |