Name
Abstract | ||
|---|---|---|
In this paper, we present a brushlet-based block matching 3D (BM3D) method to collaboratively denoise ultrasound images. Through dividing image into multiple blocks, we group them based on similarity. Then, grouped blocks sharing similarity form a 3D image volume. For each volume, brushlet thresholding is applied to remove noise in the frequency domain. Upon completion of individual filtering, the volumes are aggregated and reconstructed globally. To evaluate our method, we run our denoising scheme on synthetic images corrupted with additive or multiplicative noise. The results show that our method can achieve good denoising performance in comparison with existing methods. Our method is also evaluated on cardiac and fetal ultrasound images. Analysis on the contrast and homogeneity of the denoised images demonstrates the feasibility of applying our method to ultrasound images to improve image quality and facilitate further processing such as segmentation. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) | Brushlet, BM3D, image denoising, ultrasound imaging |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Image texture,Non-local means,Computer science,Binary image,Image processing,Image quality,Artificial intelligence,Image restoration,Thresholding,Video denoising | Conference | 1945-7928 |
Citations | PageRank | References |
4 | 0.47 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Gan | 1 | 4 | 0.80 |
| Elsa D. Angelini | 2 | 740 | 60.44 |
| Andrew F. Laine | 3 | 747 | 83.01 |
| Christine P Hendon | 4 | 6 | 1.66 |