A divide and conquer strategy for the maximum likelihood localization of low intensity objects.

Area of Science:

• Cell biology
• Image analysis
• Biophysics

Background:

• Accurate localization of sub-resolution objects is crucial in various scientific fields.
• Electron multiplying charge-coupled devices (EMCCDs) introduce excess noise, complicating object localization.
• Existing methods struggle with multiple overlapping emitters in noisy conditions.

Purpose of the Study:

• To present a novel algorithm for accurate localization of multiple overlapping sub-resolution emitters.
• To address the challenge of excess noise in image data, particularly from EMCCDs.
• To improve the scalability and robustness of object localization techniques.

Main Methods:

• Developed the Nested Maximum Likelihood Algorithm (NMLA).
• NMLA repeatedly solves single-emitter localization in an excess noise-free system.
• Compared NMLA's performance against general-purpose optimization techniques.

Main Results:

• NMLA effectively localizes multiple overlapping emitters in the presence of excess noise.
• Demonstrated significant improvements in scalability and robustness over existing methods.
• Successfully applied the algorithm for in vivo localization of fluorescent proteins.

Conclusions:

• NMLA offers a robust and scalable solution for sub-resolution object localization.
• The algorithm is particularly effective in noisy imaging conditions common in biological research.
• NMLA advances the capabilities for analyzing complex biological systems using imaging techniques.