This repository contains the code for interactive segmentation based on object creation, feature-space clustering, and flexible segmentation routines. It was originally developed as part of an image-analysis workflow, where individual objects are detected, described by shape features, clustered, and then segmented in a reproducible way.

The original archived version of this software is available on Zenodo:

• Object creation
  Functions to detect target objects, crop them, and build an object set with extracted shape features.

• Feature-space clustering
  Automatic clustering of objects in feature space (number of clusters is determined during clustering).

• Segmentation
  Modular segmentation routines that can be extended with additional methods to evaluate performance on your objects.

The original archived data file input.npy used with this code is available on Zenodo:

• DOI: https://doi.org/10.5281/zenodo.14251450

To run the full example:

1. Download input.npy from Zenodo.
2. Create a folder Workspace_data at the project root.
3. Place input.npy inside Workspace_data/.
4. Create an empty Raw_Figs folder for generated figures.

You may also create your own input.npy with the same structure to adapt the pipeline to your data.

How to run

git clone https://github.com/hamideh-h/interactive-segmentation.git cd interactive-segmentation pip install -r requirements.txt # Prepare data mkdir Workspace_data Raw_Figs # put input.npy into Workspace_data/ # Run pipeline python src/main.py ## Repository Layout

src/ main.py # entry point Segmentation.py # segmentation functions FeatureSpace_Clustering.py Object.py objectset_creation.py data/ README.md # explains where to get input.npy requirements.txt # Python dependencies