Introduction

disccofan (Distributed Connected Component Filtering and Analysis) is an advanced image processing tool designed for morphological and pattern analysis in 2-D and 3-D data sets. It is optimized to efficiently handle giga- and tera-scale data sets through extensive parallelization using OpenMP and OpenMPI.

This documentation provides comprehensive instructions on installing, using, and leveraging the full potential of disccofan.

disccofan has been featured in several scientific publications within Computer Science journals. If you utilize disccofan in your work, please ensure you cite the following paper:

• Distributed Connected Component Filtering and Analysis in 2D and 3D Tera-Scale Data Sets Simon Gazagnes and Michael H.F. Wilkinson.

For further details on the techniques implemented in disccofan, you may also acknowledge the following papers if you’re enhancing or comparing similar methodologies:

• Distributed Component Forests in 2-D: Hierarchical Image Representations Suitable for Tera-Scale Images

• Parallel Attribute Computation for Distributed Component Forests