| Title |
Fast multi-focus fusion based on deep learning for early-stage embryo image enhancement / |
| Authors |
Raudonis, Vidas ; Paulauskaite-Taraseviciene, Agne ; Sutiene, Kristina |
| DOI |
10.3390/s21030863 |
| Full Text |
|
| Is Part of |
Sensors.. Basel : MDPI. 2021, vol. 21, iss. 3, art. no. 863, p. 1-15.. ISSN 1424-8220 |
| Keywords [eng] |
image fusion ; multi-focus ; embryo development ; data reduction ; deep learning ; convolutional neural networks ; Laplacian pyramid ; correlation coefficient maximization |
| Abstract [eng] |
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 well-known 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. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
