Loading...
Please wait, while we are loading the content...
Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment
| Content Provider | MDPI |
|---|---|
| Author | Igor, Stępień Adam, Piórkowski Obuchowicz, Rafał Oszust, Mariusz |
| Copyright Year | 2021 |
| Description | The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists. |
| Starting Page | 1043 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21041043 |
| Journal | Sensors |
| Issue Number | 4 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-02-03 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Image Quality Assessment Deep Learning Network Fusion Magnetic Resonance Images |
| Content Type | Text |
| Resource Type | Article |
