Name
Title | ||
|---|---|---|
Full-reference image quality assessment-based B-mode ultrasound image similarity measure. |
Abstract | ||
|---|---|---|
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically. Yet, despite of the remarkable progress that has been made, the medical ultrasound image similarity measurement remains largely unsolved due to a high level of speckle noise contamination. Potential applications of the ultrasound image similarity measurement seem evident in several aspects. To name a few, ultrasound imaging quality assessment, abnormal function region detection, etc. In this paper, a comparative study was made on full-reference image quality assessment methods for ultrasound image visual structural similarity measure. Moreover, based on the image similarity index, a generic ultrasound motion tracking re-initialization framework is given in this work. The experiments are conducted on synthetic data and real-ultrasound liver data and the results demonstrate that, with proposed similarity-based tracking re-initialization, the mean error of landmarks tracking can be decreased from 2 mm to about 1.5 mm in the ultrasound liver sequence. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Similarity measure,Pattern recognition,Computer science,Image quality,Mean squared error,Synthetic data,Structural similarity,Artificial intelligence,Speckle noise,Match moving,Ultrasound |
DocType | Volume | Citations |
Journal | abs/1701.02797 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kele Xu | 1 | 46 | 21.80 |
| Xi Liu | 2 | 36 | 10.08 |
| Hengxing Cai | 3 | 9 | 2.21 |
| Zhifeng Gao | 4 | 143 | 13.33 |