Quantifying Image Structures in High-Throughput Microscopy with Total Variation Flow
Abstract
A recurrent problem in the analysis of microscopy images is to quantify the number and size of spots on a homogeneous background. Unfortunately, segmenting the individual spots becomes unreliable when they are close together, or when the image contains noise and artifacts. On the other hand, manual counting and line-scan measurements are prone to bias and too time-consuming to be used in high-throughput microscopy. In this work, we derive novel per-pixel measures of spot scale and density from Total Variation Flow, a partial differential equation that changes the intensities of image regions at a rate inverse to their scale. On simulated, phantom, and real-world data from Stimulated Emission Depletion (STED) microscopy, we demonstrate the robustness of our novel method relative to a standard segmentation-based approach.
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Additional Material
- Preprint (The definite version is available at ieeexplore.ieee.org) (PDF document, 596 KB)
Bibtex
@INPROCEEDINGS{GorgiZadeh:ISBI16, author = {Gorgi Zadeh, Shekoufeh and Hermann, Max and Merklinger, Elisa and Schloetel, Jan-Gero and Schultz, Thomas}, title = {Quantifying Image Structures in High-Throughput Microscopy with Total Variation Flow}, booktitle = {IEEE Symp. Biomedical Imaging (ISBI)}, year = {2016}, note = {Accepted for publication.}, abstract = {A recurrent problem in the analysis of microscopy images is to quantify the number and size of spots on a homogeneous background. Unfortunately, segmenting the individual spots becomes unreliable when they are close together, or when the image contains noise and artifacts. On the other hand, manual counting and line-scan measurements are prone to bias and too time-consuming to be used in high-throughput microscopy. In this work, we derive novel per-pixel measures of spot scale and density from Total Variation Flow, a partial differential equation that changes the intensities of image regions at a rate inverse to their scale. On simulated, phantom, and real-world data from Stimulated Emission Depletion (STED) microscopy, we demonstrate the robustness of our novel method relative to a standard segmentation-based approach.} }