About DIPlib 3

Introduction

The purpose of the DIPlib project is to provide a one-stop library and development environment for quantitative image analysis, be it applied to microscopy, radiology, astronomy, or anything in between.

There are other image processing/analysis libraries available, some of them hugely popular. Why do we keep investing time in developing and improving DIPlib? The short answer is that we believe DIPlib offers things that are not available elsewhere. The library is built on the following three principles:

  1. Precision:

    We implement the most precise known methods, and output often defaults to floating-point samples. The purpose of these algorithms is quantification, not approximation.

  2. Ease of use

    We use modern C++ features to provide a simple and intuitive interface to algorithms, with expressive syntax, default values, and little boiler-plate code required from the user. There is no need to be aware of an image's data type to use the algorithms effectively.

    Furthermore, developing an image analysis program involves a lot of trial-and-error, rapid prototyping approaches are applicable: the edit-compile-run loop should be quick. We aim for short compile times with pre-compiled algorithms and few public templates.

  3. Efficiency

    We implement the most efficient known algorithms, as long as they don't compromise precision. Ease-of-use features might also incur a slight overhead in execution times. The library can be used in high-throughput quantitative analysis pipelines, but is not designed for real-time video processing.

Algorithms in DIPlib typically accept input images of any data type (though, of course, some algorithms are specific to binary images, or cannot handle complex images, etc.) and any number of dimensions (algorithms that are limited to one specific dimensionality typically show so in their name). The image data type and dimensionality do not need to be known at compile time. Images can have pixels that are vectors or matrices, for some examples on how this relates to the three points above, see Why tensors?.

There are many other unique things about DIPlib, we encourage you to explore the documentation to learn more about it. A good place to start are the following documentation pages:

See also the examples/ directory for a series of simple C++ programs that demonstrate how to use various features of the library.

Modules, interfaces and bindings

Currently, DIPlib 3 has interfaces or bindings to the following packages:

  • MATLAB: DIPimage is a MATLAB toolbox that gives access to most functionality in DIPlib, but goes beyond that by providing a lot of additional functionality as M-file functions.
  • Python: PyDIP is a thin wrapper of most functionality in DIPlib.
  • Bio-Formats: DIPjavaio is an interface to Java-based image readers. It is designed to allow DIPlib to read hundreds of image file formats through OME Bio-Formats, but is generic enough to be used with other Java libraries as well.
  • OpenCV: the DIPlib-OpenCV interface provides copyless conversion to and from OpenCV images, for OpenCV version 2 and newer.
  • Vigra: the DIPlib-Vigra interface provides copyless conversion to and from Vigra images.

The DIPlib project further contains these additional modules:

  • DIPviewer: DIPviewer is an interactive image display utility.