The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.
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
"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.
"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.
The tool measures the total length of the microtubules in a 3D image.
You can find a test image here: http://biii.eu/node/1137
The Arabidopsis Seedlings Tool allows to analyze the germination and seedling growth of Arabidopsis (Arabidopsis thaliana) in liquid culture. It measures the surface of green pixels per well in images containing multiple wells. It can be run in batch mode on a series of images. It writes a spreadsheet file with the measured area per well and saves a control image showing the green surface that has been detected per well.
ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis.
Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional data. It is widely used and contributed in the medical imaging field.