We have identified a lack of accessible 3D/4D visualization software libraries for biological image processing. These libraries must provide (1) means to load and save any of the large diversity of image file formats (2) implementations for computer vision algorithms and (3) graphical user interfaces for data access by a human operator.
The development of novel analytical tools is greatly facilitated by the existence of well-documented software libraries.
In addition to the general requirement for visualization, the unique characteristics of each data set may demand specialized analysis. The first step in the analysis of biological image data is its visual inspection. The acquisition of large three-dimensional (3D) data sets, often as time series (4D), has become the new standard. The number of images is exploding with the availability of high-throughput and high-resolution technologies. Images are the primary data of developmental and cell biology. Life sciences are experiencing an increasing demand for scientific image processing.
Java 3d code#
We offer the source code and convenient binary packages along with extensive documentation at. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images.
Java 3d registration#
The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis.
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