Trainable Weka Segmentation

Type
Author
Ignacio Arganda-Carreras
Albert Cardona
Verena Kaynig
Johannes Schindelin
Requires
Execution Platform
Implementation Type
Programming Language
Supported image dimension
Interaction Level
License/Openness
License
GPL
Description

Quote:

The Trainable Weka Segmentation is a Fiji plugin that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations. Weka (Waikato Environment for Knowledge Analysis) can itself be called from the plugin. It contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. As described on their wikipedia site, the advantages of Weka include:

  • freely availability under the GNU General Public License
  • portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform
  • a comprehensive collection of data preprocessing and modeling techniques
  • ease of use due to its graphical user interfaces
  • Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.

The main goal of this plugin is to work as a bridge between the Machine Learning and the Image Processing fields. It provides the framework to use and, more important, compare any available classifier to perform image segmentation based on pixel classification.

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