Name
Abstract | ||
|---|---|---|
Ultrasound imaging is widely used in clinical imaging because it is non-invasive, real-time, and inexpensive. Due to the freehand nature of clinical ultrasound, analysis of an image sequence often requires registration between the images. Of the previously developed mono-modality ultrasound registration frameworks, only few were designed to register small anatomical structures. Monitoring of small finger vessels, in particular, is essential for the treatment of vascular diseases such as Raynaud's Disease. High frequency ultrasound (HFUS) can now image smaller anatomic details down to 30 microns within the vessels, but no work has been done to date on such small-scale ultrasound registration. Due to the complex internal finger structure and increased noise of HFUS, it is difficult to register 2D images of finger vascular tissue, especially under deformation. We studied a variety of similarity measurements with different pre-processing techniques to find which registration similarity metrics were best suited for HFUS vessel tracking. The overall best performance was obtained with a normalized correlation metric coupled with HFUS downsampling and a one-plus-one evolutionary optimizer, yielding a mean registration error of 0.05 mm. We also used HFUS to study how finger tissue deforms under an ultrasound transducer, comparing internal motion vs. transducer motion. Improving HFUS registration and tissue modeling may lead to new research and improved treatments for peripheral vascular disorders. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/12.2254708 | Proceedings of SPIE |
Keywords | Field | DocType |
image registration,high frequency ultrasound imaging,image processing | Transducer,Computer vision,Ultrasonic sensor,Image processing,Artificial intelligence,Anatomical structures,Small finger,Upsampling,Image registration,Ultrasound,Physics | Conference |
Volume | ISSN | Citations |
10133 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengqian Che | 1 | 0 | 0.68 |
| Jihang Wang | 2 | 0 | 0.34 |
| Vijay Gorantla | 3 | 1 | 1.05 |
| J.M. Galeotti | 4 | 75 | 13.90 |