TOMOBFLOW: feature-preserving noise filtering for electron tomography

Jose-Jesus Fernandez

doi:10.1186/1471-2105-10-178

Abstract

BackgroundNoise filtering techniques are needed in electron tomography to allow proper interpretation of datasets. The standard linear filtering techniques are characterized by a tradeoff between the amount of reduced noise and the blurring of the features of interest. On the other hand, sophisticated anisotropic nonlinear filtering techniques allow noise reduction with good preservation of structures. However, these techniques are computationally intensive and are difficult to be tuned to the problem at hand.ResultsTOMOBFLOW is a program for noise filtering with capabilities of preservation of biologically relevant information. It is an efficient implementation of the Beltrami flow, a nonlinear filtering method that locally tunes the strength of the smoothing according to an edge indicator based on geometry properties. The fact that this method does not have free parameters hard to be tuned makes TOMOBFLOW a user-friendly filtering program equipped with the power of diffusion-based filtering methods. Furthermore, TOMOBFLOW is provided with abilities to deal with different types and formats of images in order to make it useful for electron tomography in particular and bioimaging in general.ConclusionTOMOBFLOW allows efficient noise filtering of bioimaging datasets with preservation of the features of interest, thereby yielding data better suited for post-processing, visualization and interpretation. It is available at the web site .

Highlights

Noise filtering techniques are needed in electron tomography to allow proper interpretation of datasets
The performance of TOMOBFLOW is illustrated with its application to a number of experimental datasets obtained from electron tomography
Different contrast and signal-to-noise ratio were present in those datasets as they were obtained by using different preparation techniques

Summary

Introduction

Noise filtering techniques are needed in electron tomography to allow proper interpretation of datasets. On the other hand, sophisticated anisotropic nonlinear filtering techniques allow noise reduction with good preservation of structures. These techniques are computationally intensive and are difficult to be tuned to the problem at hand. Is the need for advanced image processing methods that facilitate analysis and interpretation at different scales of resolution and complexity. Electron tomography (ET), which combines electron microscopy with the power of three-dimensional (3D) imaging, is the leading technique to elucidate the molecular architecture of biological specimens in a close-tonative state [1,2,3]. Similar filtering needs arise in other bioimaging modalities (e.g. [5,6,7,8])

Results

Discussion

Conclusion