Resolution-Free Accurate DNA Contour Length Estimation from Atomic Force Microscopy Images.

This research presented an accurate and efficient contour length estimation method developed for DNA digital curves acquired from Atomic Force Microscopy (AFM) images. This automation method is calibrated against different AFM resolutions and ideal to be extended to all different kinds of biopolymer samples, encompassing all different sample stiffnesses. The methodology considers the digital curve local geometric relationship, as these digital shape segments and pixel connections represent the actual morphology of the biopolymer sample as it is being imaged from the AFM scanning. In order to incorporate the true local geometry relationship that is embedded in the continuous form of the original sample, one needs to find this geometry counterpart in the digitized image. This counterpart is realized by taking the skeleton backbone of the sample contour and by using these digitized pixels' connection relationship to find its local shape representation. In this research, one uses the 8-connect Freeman Chain Code (CC) to describe the directional connection between DNA image pixels, in order to account for the local shapes of four connected pixels. The result is a novel shape number (SN) system derived from CC, which is a fully automated algorithm that can be applied to DNA samples of any length for accurate estimation, with efficient computational cost. This shape-wise consideration is weighted to modify the local length with great precision, accounting for all the different morphologies of the biopolymer sample, and resulted with accurate length estimation, as the error falls below 0.07%, an order of magnitude improvement compared to previous findings.

Local raster scanning for high-speed imaging of biopolymers in atomic force microscopy.

A novel algorithm is described and illustrated for high speed imaging of biopolymers and other stringlike samples using atomic force microscopy. The method uses the measurements in real-time to steer the tip of the instrument to localize the scanning area over the sample of interest. Depending on the sample, the scan time can be reduced by an order of magnitude or more while maintaining image resolution. Images are generated by interpolating the non-raster data using a modified Kriging algorithm. The method is demonstrated using physical simulations that include actuator and cantilever dynamics, nonlinear tip-sample interactions, and measurement noise as well as through scanning experiments in which a two-axis nanopositioning stage is steered by the algorithm using simulated height data.