Szczegóły publikacji
Opis bibliograficzny
Fusion of deep convolutional neural networks for no-reference magnetic resonance image quality assessment / Igor Stępień, Rafał Obuchowicz, Adam PIÓRKOWSKI, Mariusz Oszust // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2021 — vol. 21 iss. 4 art. no. 1043, s. 1-16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14-16, Abstr. — Publikacja dostępna online od: 2021-02-03
Autorzy (4)
- Stępień Igor
- Obuchowicz Rafał
- AGHPiórkowski Adam
- Oszust Mariusz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 132449 |
|---|---|
| Data dodania do BaDAP | 2021-02-08 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s21041043 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
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.
