Abstract
Microscopy is a valuable imaging tool in various biomedical research areas. Recent developments have made high resolution acquisition possible within a relatively short time. State-of-the-art imaging equipment such as serial block- face electron microscopes acquire gigabytes of data in a matter of hours. In order to make these amounts of data manageable, a more data-efficient representation is required. A popular approach for such data efficiency are superpixels which are designed to cluster homogeneous regions without crossing object boundaries. The use of superpixels as a pre-processing step has shown significant improvements in making computationally intensive computer vision analysis algorithms more tractable on large amounts of data. However, microscopy datasets in particular can be degraded by noise and most superpixel algorithms do not take this artifact into account. In this paper, we give a quantitative and qualitative comparison of superpixels generated on original and denoised images. We show that several advanced superpixel techniques are hampered by noise artifacts and require denoising and parameter tuning as a pre-processing step. The evaluation is performed on the Berkeley segmentation dataset as well as on fluorescence and scanning electron microscopy data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bene Miroslav, Zitova Barbar. Perlormance evaluation of image segmentation algorithms on microscopic image data Journal of M icroscopy. 2015;257:65-85.
Osher Stanley, Paragios Nikos. Geometric Level Set Methods in Imilging, Vision, and Graphics. Springer 2003.
Helmstaedter Moritz, Briggman Kevin L., Turaga Srinivas C., Jain Viren, Seung H. Sebastian, Denk Winfried. Connectomic reconstruction of the inner plexiform layer in the mouse retina Nature. 2013.
Jezierska Anna, Talbot Hugues. Poisson-Gaussian Noise Parameter Estimation in Fluorescence Microscopy Imaging IEEE International Symposium on Biomedical Imllging. 2012:1663-1666.
Roels Joris, Aelterman Jan, De Vylder Jonas, et al. Noise Analysis in 3D Electron Microscopy in Proc. 5th Dutch Bio-Mediml Engineering Conference 2015.
Roels Joris, Aelterman Jan, De Vylder Jonas, et al. Noise Analysis and Removal in 3D Electron Microscopy Lecture Notes in Computer Science (Advances in Visual Computing). 2014:31-40.
Lucchi Aurelien, Smith Kevin, Achanta Radhakrishna, Lepetit Vincent, Fua Pascal. A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images. Proc. International Conference on Medical Imilge Computing and Computer-Assisted Intervention. 2010;13:463-471.
Jain Viren, Turaga Srinivas C.. Learning to Agglomerate Super-pixel Hierarchies Advances in Neural Informiltion Processing Systems. 2011:1-9.
Navlakha Saket, Ahammad Parvez, Myers Eugene W. Unsupervised Segmentation of Noisy Electron Microscopy Images using Salient Watersheds and Region Merging. BMC Bioinformlltics. 2013;14:294.
Arbehiez Pablo, Maire Michael, Fowlkes Charless, Malik Jitendra. Contour detection and hierarchical image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011;33:898-916.
Moore Alastair P., Prince Simon J. D., Warrell Jonathan, Mohammed Umar, Jones Graham. Superpixel Lattices Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2008:1-8.
Levinshtein Alex, Stere Adrian, Kutulakos Kiriakos N., Fleet David J., Dickinson Sven J., Siddiqi Kaleem. TurboPixels: Fast Superpixels using Geometric Flows. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009;31:2290-7.
Achanta Radhakrishna, Shaji Appu, Smith Kevin, Lucchi Aurelien. SLIC Superpixels Compared to State-of-the-art Superpixel Methods IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34:2274-2281.
Comaniciu Dorin, Meer Peter. Mean Shift: A Robust Approach Toward Feature Space Analysis IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002;24:603-619.
Felzenszwalb Pedro F., Huttenlocher Daniel P. Efficient Graph-Based Image Segmentation International Journal of Computer Vision. 2004;59:167-181.
Goossens Bart, Pi urica Aleksandra, Philips Wilfried. Image Denoising using Mixtures of Projected Gaussian Scale Mixtures IEEE Transactions on Imilge Processing. 2009;18:1689-1702.
Buades Antoni, Coil Bartomeu, Morel Jean-Michel. A Non-local Algorithm for ImageDenoising in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition;2:60-65 vol. 2 2005.
Dabov Kostadin, Foi Alessandro. Image Denoising with Block-matching and 3D Filtering Electronic Imllging. 2006;6064:1-12.
Aelterman Jan, Goossens Bart. Combined Non-local and Multi- Resolution Sparsity Prior in Image Restoration IEEE Transactions on Imilge Processing. 2012:3049-3052.
Huttenlocher DanielP., Klanderman Gregory A., Rucklidge William J. Comparing images using the Hausdorff distance IEEE Transactions on Pattern Analysis and Machine Intelligence. 1993;15:850-863.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Roels, J., De Vylder, J., Aelterman, J., Lippens, S., Saeys, Y., Philips, W. (2016). Superpixel Quality in Microscopy Images: The Impact of Noise & Denoising. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)