Name
Title | ||
|---|---|---|
Medical Image Registration via Similarity Measure based on Convolutional Neural Network |
Abstract | ||
|---|---|---|
Registration, which is exploited to establish the corresponding relationship between a group of images, is of importance for medical applications. Within the image processing process, a similarity measure is an essential stage. To note that the effectiveness of similarity measure is to evaluate the discrepancy between a set of image slices, which greatly affects the performance of registration. Most of the previous algorithms can be categorized in model-based methods, which rely on their suitability to the images. Meanwhile, these similarity measures can not satisfy the requirements of efficiency and accuracy in medical image registration. To address the above-mentioned problems, one novel similarity measure is presented with a convolutional neural network. Experiments were conducted to evaluate the proposed similarity measure with two public DIARETDB1 and RIRE. The numerical and visual outcome both support our work. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CloudTech49835.2020.9365908 | 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) |
Keywords | DocType | ISBN |
Deep learning,Registration,Retinal image | Conference | 978-1-7281-6176-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Li | 1 | 475 | 67.20 |
| Yongzheng Lin | 2 | 0 | 0.34 |
| Yishen Pang | 3 | 0 | 0.68 |