Compressed sensing denoising for segmentation of localization microscopy data

Abstract

Localization microscopy (LM) allows to acquire pointillistic superresolution images of biological structures on the nanoscale. However, current structure reconstruction and segmentation approaches suffer from either exclusion of small structures or strong dependence on a-priori knowledge. We propose reconstruction methods based on compressed sensing (CS) denoising in combination with the isodata threshold for segmentation. The methods are verified on artificial test data. For the denoising, a Haar dictionary and a KSVD dictionary learning on artificial data are used. Both methods perform significantly better than the reference algorithm, a linear density filter, in terms of root-mean-square deviation from the ground truth. Furthermore, exemplary results on real LM data of irradiated cell nuclei with Heterochromatin labeling make small structures visible that are suppressed by the reference method. CS denoising demonstrates promising results for reconstruction of LM data.