Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Autor: Vidas Raudonis, Agne Paulauskaite-Taraseviciene, Kristina Sutiene
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Sensors, Vol 21, Iss 3, p 863 (2021)
Druh dokumentu: article
ISSN: 21030863
1424-8220
DOI: 10.3390/s21030863
Popis: Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje