Understanding MIPAV capabilities

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MIPAV Algorithms Overview

Introducing MIPAV

Imaging is an essential component in many fields of medical research and clinical practice. Biologists study cells and generate three-dimensional (3D) confocal microscopy datasets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors from Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT) scans, and neuroscientists detect regional metabolic brain activity from Positron Emission Tomography (PET) and functional MRI scans. Analysis of these diverse types of images requires sophisticated computerized quantification and visualization tools. Until recently, 3D visualization of images and quantitative analyses could only be performed using expensive UNIX workstations and customized software. Because of technological advancements, much of the today's visualization and analysis can be performed on an inexpensive desktop computer equipped with the appropriate graphics hardware and software. The Medical Image Processing, Analysis, and Visualization (MIPAV) software application takes advantage of the recent technological advancements, while offering functionality specifically designed for the medical community.

MIPAV is an extensible image processing, analysis, and visualization software. Because MIPAV is Java-based, it can run on virtually any platform. It can also accommodate n-dimensional images in over 20 different file formats, including DICOM, Analyze, TIFF, JPG, and RAW.

MIPAV is designed to be both a software application and an application programming interface (API). As a software application, MIPAV provides an easy-to-use graphical user interface (GUI) that allows researchers to extract quantitative information from various medical imaging modalities.

Understanding MIPAV capabilities

MIPAV provides ready-made, general-purpose tools that meet the majority of requirements of many researchers. Researchers can use these tools to perform a variety of tasks:

  • View image files, juxtapose images, and adjust opacity level so that overlapping areas can be studied
  • Create image files and view and modify the attributes of an image, including DICOM and volume of interest (VOI) information
  • Adjust the display of an image file and adjust magnification settings
  • View DICOM overlays and sanitize DICOM information and send and receive image files to and from databases residing on DICOM-compliant servers (on computer or imaging device)
  • Manually, semiautomatically, and automatically delineate or segment volumes of interest (VOI) and generate graphs and run statistics on VOIs<
  • Generate and adjust histograms and lookup tables (LUT) using customized or preset options
  • Run sophisticated, predefined algorithms and generate a log of each algorithm run for each image
  • Create new plug-ins to further customize the analysis of data
  • Save transformation, look-up table (LUT), and VOI data and apply them to other images
  • Print image files, graphs, statistical data, algorithmic logs, and debugging log data

Developing new tools using the API