Localization and Image Reconstruction in a STORM Based Super-resolution Microscope

• article
• demo
• archive

BibTeX info

Communicated by Federico Lecumberry and Xavier Bou
Demo edited by Xavier Bou

Abstract

In this paper, we present a comprehensive Python program for localizing the point spread functions (PSFs) present in a stack of images and thereby rendering a super-resolved image in a Stochastic Optical Reconstruction Microscopy (STORM). A microscope that provides super-resolved images is known as a super-resolution microscope. Optical super-resolution microscopy is playing a pivotal role in advancing the field of optical imaging and has found applications in a number of areas such as cellular biology, biotechnology, medical research, and nanotechnology. The proposed Python program utilizes image processing techniques to accurately identify the PSFs present in highly noisy images with densely packed fluorescent objects. Our program not only provides all the necessary tools for image reconstruction in a STORM microscope under open source license but also offers certain advantages over the existing reconstruction software packages. Some such advantages are an option to start the reconstruction process and the visualization of the rendered super-resolved image in parallel with image acquisition and disposal of the images immediately after acquisition for minimum use of disk space. Parallel visualization of the reconstructed image allows aborting the image acquisition in the case the images are not suitable for super-resolution, thereby saving valuable time. Our Python program is demonstrated using a number of different image stacks. The proposed software code can be applied not only to STORM but also to any other super-resolution technique using single-molecule localization.

Download

• full text manuscript: PDF (2.7MB)
• source code: ZIP