Name
Abstract | ||
|---|---|---|
Persistence, also called temporal averaging, takes the weighted average between successive image frames to reduce the ultrasound speckle noise. Conventional methods apply persistence coefficients as a function of two image pixels at the same location of two successive frames that always make structure blurring during tissue movement. In this paper we apply the motion analysis technique in a way of compensating the internal tissue motions to have accurate image registration such that the persistence works at the right spatial positions without blurring the tissue structure. To meet the real-time requirement of ultrasound imaging, we use the sum-absolute-difference based block matching method for local motion estimation, followed by motion dependent persistence averaging. In vivo tests show that our method can increase signal-to-noise ratio of imaging and also present better edge differentiation in structure regions |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.221 | ICPR (3) |
Keywords | Field | DocType |
successive image frame,adaptive persistence utilizing motion,motion compensation,motion analysis,image matching,persistence coefficient,temporal averaging,biomedical ultrasonics,sum-absolute-difference based block matching,image pixel,ultrasound images,image denoising,accurate image registration,motion estimation,motion analysis technique,persistence work,adaptive persistence,local motion estimation,image registration,medical image processing,internal tissue motion,structure region,motion dependent persistence averaging,real time,speckle noise,ultrasound,signal to noise ratio | Computer vision,Pattern recognition,Computer science,Motion compensation,Ultrasound imaging,Artificial intelligence,Pixel,Motion analysis,Speckle noise,Motion estimation,Image registration,Ultrasound | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-7695-2521-0 |
Citations | PageRank | References |
1 | 0.47 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Wang | 1 | 1 | 0.81 |
| Dong C. Liu | 2 | 30 | 4.72 |