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Retracted : Localizing and extracting filament distributions from microscopy images
Author(s) -
BASU S.,
DAHL K.N.,
ROHDE G.K.
Publication year - 2013
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12018
Subject(s) - protein filament , microscopy , biological system , confocal , confocal microscopy , microscope , computer science , pixel , actin cytoskeleton , filter (signal processing) , cytoskeleton , artificial intelligence , pattern recognition (psychology) , materials science , optics , physics , computer vision , biology , genetics , cell , composite material
Summary Detailed quantitative measurements of biological filament networks represent a crucial step in understanding architecture and structure of cells and tissues, which in turn explain important biological events such as wound healing and cancer metastases. Confocal microscope images of biological specimens marked for different structural proteins constitute an important source for observing and measuring meaningful parameters of biological networks. Unfortunately, current efforts at quantitative estimation of architecture and orientation of biological filament networks from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here we describe a new method for localizing and extracting filament distributions from 2D confocal microscopy images. The method combines a filter‐based detection of pixels likely to contain a filament with a constrained reverse diffusion‐based approach for localizing the filaments centrelines. We show with qualitative and quantitative experiments, using both simulated and real data, that the new method can provide more accurate centreline estimates of filament in comparison to other approaches currently available. In addition, we show the algorithm is more robust with respect to variations in the initial filter‐based filament detection step often used. We demonstrate the application of the method in extracting quantitative parameters from an experiment that seeks to quantify the effects of carbon nanotubes on actin cytoskeleton in live HeLa cells. We show that their presence can disrupt the overall actin cytoskeletal organization in such cells.