# Image thresholding

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.

- Read more about NET - Network Extraction Tool
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"we present a new fully automated 3D reconstruction algorithm, called TReMAP, short for Tracing, Reverse Mapping and Assembling of 2D Projections. Instead of tracing a 3D image directly in the 3D space as seen in majority of the tracing methods, we first trace the 2D projection trees in 2Dplanes, followed by reverse-mapping the resulting 2D tracing results back into the 3D space as 3D curves; then we use a minimal spanning tree (MST) method to assemble all the 3D curves to generate the final 3D reconstruction. Because we simplify a 3D reconstruction problem into 2D, the computational costs are reduced dramatically."

Suitable for high throughput neuron image analysis (image sizes >10GB). This plugin can be used with default parameters or user-defined parameters.

- Read more about TReMAP
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"We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal- covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly)"

This plugin can be used with default parameters or user-defined parameters.

- Read more about APP (All-path pruning)
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This is an ImageJ plugin to analyze bacterial cells. It provides a user-friendly interface and a powerful suite of detection, analysis and data presentation tools. It works with individual phase or fluorescence images as well as stacks, hyperstacks, and folders of any of these types. Even large image sets are analyzed rapidly generating raw tabular data that can either be saved or copied as is, or have additional statistical analysis performed and graphically represented directly from within MicrobeJ, making it an all-in-one image analysis solution.

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Adiposoft is an automated Open Source software for the analysis of adipose tissue cellularity in histological sections.

Example data can be found on the plugin description page in ImageJ wiki (download link). There is also a link to a MATLAB version of the workflow.

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