SLT-LoG: A Vesicle Segmentation Method with Automatic Scale Selection and Local Thresholding Applied to TIRF Microscopy

Abstract : Accurately detecting cellular structures in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking or classification. We aim at segmenting vesicles in TIRF images. The optimal segmentation scale is automatically selected, relying on a multiscale feature detection stage, and the segmentation consists in thresholding the Laplacian of Gaussian of the intensity image. In contrast to other methods, the threshold is locally adapted, resulting in better detection rates for complex images. Our method is mostly on par with machine learning-based techniques, while offering lower computation time and requiring no prior training. It is very competitive with existing unsupervised detection algorithms.
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Conference papers
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https://hal.inria.fr/hal-00921793
Contributor : Antoine Basset <>
Submitted on : Saturday, December 21, 2013 - 2:57:24 PM
Last modification on : Tuesday, May 28, 2019 - 1:40:50 AM

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  • HAL Id : hal-00921793, version 1

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Antoine Basset, Jérôme Boulanger, Patrick Bouthemy, Charles Kervrann, Jean Salamero. SLT-LoG: A Vesicle Segmentation Method with Automatic Scale Selection and Local Thresholding Applied to TIRF Microscopy. ISBI - 2014 IEEE International Symposium on Biomedical Imaging, Apr 2014, Beijing, China. ⟨hal-00921793⟩

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