Image Processing and Scientific Visualization are two separate processes
within the scientific research lifecycle, yet the two concepts often play off of one
another. Image processing refers to the enhancement and transformation of images to
prepare them for quantitative analysis. Scientific visualization is the graphical communication
of data so that trends and anomalies can be more easily recognized. UVa Research Computing
offers many services and resources to help researchers augment their work with image
processing and scientific visualization techniques.
Image processing encompasses a variety of techniques to prepare images for analysis.
Researchers often need to remove noise artifacts from their imaging data, or they need to
analyze particular regions of interest. While manual image manipulation can easily yield
the desired results, this can be time-consuming or even impossible with the amount of data
we are able to collect with high throughput screening. By automating image processing steps
such as noise filtering and segmentation, researchers are able to perform their work faster
and for larger quantities of data.
Common Image Processing Techniques
The following techniques are commonly employed in imaging research. All of these processes
can be automated and run locally on your computer or on Rivanna, UVa’s high performance
computing (HPC) cluster. With the parallelization capabilities of HPC, it is possible to
fully process and analyze a large imaging data set in a few hours or less!
Image preprocessing can help enhance the quality of your images. Common preprocessing techniques include adjusting brightness and contrast, removing noise, sharpening images, and performing geometric and color transformations.
Image segmentation is useful for determining one or multiple regions of interest. Segmentation can be used to identify foreground objects, cell boundaries, or tissue types.
Image registration is useful when comparing two or more objects of differing size or morphological features. Registration can be used to align 2D or 3D images through linear or non-linear algorithms.
Image analysis is the measurement and statistical analysis of meaningful features in your imaging data, such as area or volume of a region of interest and mean intensity value throughout an image.
ImageJ/Fiji - ImageJ is a Java-based image processing program developed at the NIH.
ImageJ can be used interactively through a graphical user interface or automatically with
Java. Fiji is ImageJ with common plugins pre-installed for scientific image analysis.
MATLAB - Matlab is a numerical computing environment with its own proprietary
programming language. Matlab provides an extensive Image Processing Toolbox for with
built-in functions for image registration, segmentation, and analysis.
Python - Python is a powerful high-level programming language for general purpose
programming. There are several open source packages available in Python for image
processing, including: OpenCV, scikit-image, and Python Imaging Library.
ANTs - ANTs, or Advanced Normalization Tools, is a state-of-the-art medical image
registration and segmentation toolkit. ANTs works in conjunction with Insight Toolkit
(ITK) to read and visualize multidimensional imaging data.
R - R is an open source programming language and computing environment for
statistical analysis and data visualization. There are a variety of R packages available
for image processing, such as ANTsR, EBImage, and magick.
We currently offer online tutorials for image processing with Fiji/ImageJ.
Stay tuned for additional online tutorials as well as in-person workshops listed on our workshops page
Visualization is the conversion of data into plots or images in order to view various
features of the data. As humans, we are able to absorb large amounts of information through
sight. We can use visualizations as an exploratory tool to gain insight into the data we
collect and to create hypotheses for relationships. We can also use visualizations to
communicate ideas to others.
MATLAB - MATLAB contains many built-in functions for data visualization, including those
for 3D surfaces and meshes. MATLAB is also capable of medical image visualization and is
compatible with DICOM and NIFTI filetypes.
ParaView - ParaView is an open-source application for visualization and analysis of
data defined on meshes or grids. It allows for visualization of 2D or 3D data and is good
for general purpose, rapid visualization.
VisIt - VisIt is software for the visualization of data defined on meshes or grids. It is
compatible with file types that have an underlying HDF5 format.
Blender - Blender is a 3D graphics software that can be used for creating 3D objects and
animations. It can be used for 3D modeling, rendering, motion tracking, and video
Unity - Unity is a cross-platform software application for the creation of visualizations
in augmented and virtual reality.
We currently offer several online tutorials for data visualization.
Stay tuned for additional online tutorials as well as our workshops posted on our workshops page