Szczegóły publikacji
Opis bibliograficzny
Magnetic resonance image quality assessment by using non-maximum suppression and entropy analysis / Rafał Obuchowicz, Mariusz Oszust, Marzena BIELECKA, Andrzej BIELECKI, Adam PIÓRKOWSKI // Entropy [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1099-4300. — 2020 — vol. 22 iss. 2 art. no. 220, s. 1–12. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 10–12, Abstr. — Publikacja dostępna online od: 2020-02-16
Autorzy (5)
- Obuchowicz Rafał
- Oszust Mariusz
- AGHBielecka Marzena
- AGHBielecki Andrzej
- AGHPiórkowski Adam
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 127660 |
|---|---|
| Data dodania do BaDAP | 2020-03-03 |
| Tekst źródłowy | URL |
| DOI | 10.3390/e22020220 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Entropy |
Abstract
An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.
