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Clock Scan Protocol for Image Analysis: ImageJ Plugins
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FunImageJ: a Lisp framework for scientific image processing.

Kyle I S Harrington1, Curtis T Rueden2, Kevin W Eliceiri2,3

  • 1Computational and Physical Systems Group, Virtual Technology and Design, University of Idaho, Moscow, ID, USA.

Bioinformatics (Oxford, England)
|November 7, 2017
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Summary
This summary is machine-generated.

FunImageJ offers a Lisp framework for efficient scientific image processing in big data biological analysis. It provides a functional programming style within the ImageJ ecosystem.

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Area of Science:

  • * Computational Biology
  • * Bioinformatics
  • * Image Analysis

Background:

  • * Biological image analysis often involves processing large datasets, requiring efficient computational tools.
  • * Existing image processing frameworks may lack flexibility or performance for complex big data tasks.

Purpose of the Study:

  • * To introduce FunImageJ, a Lisp framework designed for scientific image processing.
  • * To provide a functional programming approach for biological image analysis within the ImageJ ecosystem.
  • * To address the performance demands of big data processing in this field.

Main Methods:

  • * Development of a Lisp-based framework integrated with the ImageJ software.
  • * Implementation of functional programming paradigms for image processing tasks.
  • * Optimization for handling large-scale biological image datasets.

Main Results:

  • * FunImageJ enables a natural functional-style programming for image analysis.
  • * The framework is built upon the robust ImageJ software ecosystem.
  • * It meets the performance requirements for big data processing in biological imaging.

Conclusions:

  • * FunImageJ enhances the capabilities of ImageJ for advanced scientific image processing.
  • * The framework facilitates efficient and flexible analysis of large biological image datasets.
  • * It represents a valuable tool for researchers in computational biology and bioinformatics.