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Our work aims at using quantitative imaging tools to complement thelimitation of noise encountered by high resolution fluorescence microscopymethods. Several cycles of fluorophore activation, imaging and deactivationproduce a sequence of images in which the signals of individual fluorophores donot overlap, due to the low light intensity during their activation. Thecentroid position of each fluorophore is then determined by Gaussian fitting ofeach signal, where the final resolution depends on the precision with whicheach fluorphore is localized. Superimposing the images will result in havingthe same fluorophore mapped onto a `cloud' of locations. The most significantinformation of the superimposed images is contained in the macro-structuresidentifying microtubules, mitochondria or other organelles. Cascades of binaryimage processing algorithms are applied in order to isolate the largerorganelles. A Markovian algorithm selecting the nearest neighbour is finallyapplied to the de-noised images, to automatically extract relevant informationon microtubules. Our work supplements advancements in experimental technologieswith computational methods, helping quantifying sub-cellular properties withhigh accuracy.

Microtubule tracking from stochastic optical reconstruction microscopy images