Abstract
Automated image analysis provides a powerful tool for detecting and tracking fluorophore spots in fluorescence microscopy images. The validation of automated spot detection methods requires ground-truth data. Here, a simple framework is proposed for generating 3D fluorescence microscopy images with real background and synthetic spots, forming realistic, synthetic images with ground-truth information. Similarity between synthetic and real images was evaluated using similarity criteria, such as visual comparison, central moments with Student’s t test and intensity histograms. Student’s t test shows that there is no statistical difference between central moment features of the real and synthetic images and the intensity histograms exhibit similar shapes, demonstrating high similarity between real images and the synthetic images. The performance of four detection methods using synthetic images (with real background and no background) created using the proposed framework was also compared. \(F_{\text{score}}\) values were higher on synthetic images with no background compared to those with a real background indicating that the presence of the background reduces the effectiveness of the spot detection methods.
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Acknowledgements
Financial support was received from Council for Scientific and Industrial Research (CSIR) and the Electrical and Electronic Engineering Department at the University of Johannesburg. We would also like to thank the Synthetic Biology research group at the CSIR for providing us with real microscopy images.
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Mabaso, M., Withey, D., Twala, B. (2018). Generation of 3D Realistic Synthetic Image Datasets for Spot Detection Evaluation. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_6
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DOI: https://doi.org/10.1007/978-981-10-7868-2_6
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