Abstract
Lens-free holographic imaging is important in many biomedical applications, as it offers a wider field of view, more mechanical robustness and lower cost than traditional microscopes. In many cases, it is important to be able to detect biological objects, such as blood cells, in microscopic images. However, state-of-the-art object detection methods are not designed to work on holographic images. Typically, the hologram must first be reconstructed into an image of the specimen, given a priori knowledge of the distance between the specimen and sensor, and standard object detection methods can then be used to detect objects in the reconstructed image. This paper describes a method for detecting objects directly in holograms while jointly reconstructing the image. This is achieved by assuming a sparse convolutional model for the objects being imaged and modeling the diffraction process responsible for generating the recorded hologram. This paper also describes an unsupervised method for training the convolutional templates, shows that the proposed method produces promising results for detecting white blood cells in holographic images, and demonstrates that the proposed object detection method is robust to errors in estimated focal depth.
Keywords
- Holography
- Convolutional sparse coding
- Phase recovery
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Haeffele, B.D., Pick, C., Lin, Z., Mathieu, E., Ray, S.C., Vidal, R.: An optical model of whole blood for detecting platelets in lens-free images. In: Workshop on Simulation and Synthesis for Medical Imaging (at MICCAI). Springer (2019)
Haeffele, B.D., Stahl, R., Vanmeerbeeck, G., Vidal, R.: Efficient reconstruction of holographic lens-free images by sparse phase recovery. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 109–117. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_13
Kim, M.K.: Digital Holographic Microscopy. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-7793-9
Liu, Y., Zhan, Z., Cai, J.F., Guo, D., Chen, Z., Qu, X.: Projected iterative soft-thresholding algorithm for tight frames in compressed sensing magnetic resonance imaging. IEEE Trans. Med. Imaging 35(9), 2130–2140 (2016)
Papyan, V., Sulam, J., Elad, M.: Working locally thinking globally: theoretical guarantees for convolutional sparse coding. IEEE Trans. Signal Process. 65(21), 5687–5701 (2017)
Plaut, E., Giryes, R.: Matching pursuit based convolutional sparse coding. In: IEEE ICASSP, pp. 6847–6851 (2018)
Seo, S., Isikman, S.O., Sencan, I., Mudanyali, O., Su, T.W., Bishara, W., Erlinger, A., Ozcan, A.: High-throughput lens-free blood analysis on a chip. Anal. Chem. 82(11), 4621–4627 (2010)
Seo, S., et al.: Lensfree holographic imaging for on-chip cytometry and diagnostics. Lab Chip 9(6), 777–787 (2009)
Yellin, F., Haeffele, B., Vidal, R.: Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding. In: IEEE International Symposium on Biomedical Imaging, pp. 650–653 (2017)
Yellin, F., Haeffele, B., Vidal, R.: Multi-cell classification by convolutional dictionary learning with class proportion priors. In: IEEE Conference on CVPR (2018)
Acknowledgments
This work was funded by miDiagnostics.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yellin, F. et al. (2019). Joint Holographic Detection and Reconstruction. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_76
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)
-
Published in cooperation with
http://miccai.org/
