filament

The ultimate goal of the NET framework is to make images of networks processable by computers. Therefore we want to have a pixel based image as input, as output we want a representation of the network visible in the image that retains as much information about the original network as possible. NET achives this by first segmenting the image and then vectorizing the network and then extracting information. The information we extract is

• First and foremost the graph of the network. We find the crossings (nodes) and connections between crossings (edges) and therefore extract information about the neighborhood relations, the topology of the network.
• We also extract the coordinates of all nodes which enables us to embed them into space. We therefore extract information about the geometry of the network.
• Last but not least we track the radii of the edges in the extraction process. Therefore every edge has a radius which can be identified with its conductivity.

In the following we will first provide detailed instructions on how to install NET on several platforms. Then we describe the functionality and options of each of the four scripts that make up the NET framework.

ORION: Online Reconstruction and functional Imaging Of Neurons: segmentation and tracing of neurons for reconstruction.

A project to develop tools that explore single neuron function via sophisticated image analysis. ORION software bridges advanced optical imaging and compartmental modeling of neuronal function by rapidly, accurately, and robustly generating, from structural image data, a cylindrical morphology model suitable for simulating neuronal function. The goal of this project is to develop a computational and experimental framework to allow real-time mapping of functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellularions) to neuronal structure, during the very limited duration of an acute experiment.

This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length.

Tags are shown in a new window displaying every tag in a different color. You can find it under [Plugins>Skeleton>Analyze Skeleton (2D/3D)]. See Skeletonize3D for an example of how to produce skeleton images.

The voxels are classified into three different categories depending on their 26 neighbors: - End-point voxels: if they have less than 2 neighbors. - Junction voxels: if they have more than 2 neighbors. - Slab voxels: if they have exactly 2 neighbors.

End-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.

Notice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other.

Output data type: table result, image of the skeleton

SOAX is an open source software tool to extract the centerlines, junctions and filament lengths of biopolymer networks in 2D and 3D images. It facilitates quantitative, reproducible and objective analysis of the image data. The underlying method of SOAX uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then stretch along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments.

SOAX provides 3D visualization for exploring image data and visually checking results against the image. Quantitative analysis functions based on extracted networks are also implemented in SOAX, including spatial distribution, orientation, and curvature of filamentous structures. SOAX also provides interactive manual editing to further improve the extraction results, which can be saved in a file for archiving or further analysis. Useful for microtubules or actin filaments.

Observation: Depending on the operating system, the installation may or may not require Boost C++, ITK and VTK libraries. Windows has a standalone executable application without the need of those. 

Image-processing algorithms developed at the MOSAIC Group for fluorescence microscopy. Tools included:

• 2D/3D single-particle tracking tool which can be used to track bright spots in 2D/3D movies over time.
• Optimal filament segmentation of 2D images. 
• Curvature filters for image filtering, denoising, and restoration. 
• Image naturalization for image enhancement based on gradient statistics of natural-scence images. 
• Tool for automatically send and distribute jobs on clusters and get back the results.
• Multi-region image segmentation of 2D and 3D images without needing to know the number of regions beforehand. 
• Squassh for globally optimal segmentation of piecewise constant regions in 2D and 3D images and for object-based co-localization analysis. 
• Tool for inferring spatial interactions between patterns of objects in images or between coordinates read from a file.
• Tool for robust, histogram-based background subtraction well suited to correct for inhomogeneous illumination artifacts.
• A tool to estimate the Point-Spread Function of the microscopy out of 2D fluorescence images.
• A tool to measure the 3D Point-Spread Function of a confocal microscope from an image stack.
• Addition of synthetic Poisson-distributed noise to an image in order to simulate shot noise of various signal-to-noise ratios. 
• Convolution of an image with a Bessel function in order to simulate imaging with a microscope. 
• A utility to detect bright spots in images and estimate their center. 
• A utility to create manual segmentations to be used as ground truth to test and benchmark automatic segmentation algorithms.
• A tool for replacing one color in an image with another color.