Cargando…
Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment
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 jo...
Autores principales: | Stępień, Igor, Obuchowicz, Rafał, Piórkowski, Adam, Oszust, Mariusz |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913522/ https://www.ncbi.nlm.nih.gov/pubmed/33546412 http://dx.doi.org/10.3390/s21041043 |
Ejemplares similares
-
Interobserver variability in quality assessment of magnetic resonance images
por: Obuchowicz, Rafal, et al.
Publicado: (2020) -
Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis
por: Obuchowicz, Rafał, et al.
Publicado: (2020) -
A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images
por: Stępień, Igor, et al.
Publicado: (2022) -
Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging
por: Strzelecki, Michał, et al.
Publicado: (2022) -
Universal Measure for Medical Image Quality Evaluation Based on Gradient Approach
por: Bielecka, Marzena, et al.
Publicado: (2020)