Interactive Segmentation Quality Evaluator

Educational tool for understanding segmentation metrics through hands-on boundary drawing and evaluation

Overview

Project Repository MATLAB File Exchange

An interactive MATLAB application that teaches image segmentation evaluation metrics through hands-on boundary drawing and real-time quality assessment against ground truth references.

Educational Focus: Image Segmentation, Quality Metrics, Computer Vision Fundamentals

How It Works

The tool provides:

  • Interactive drawing: Draw segmentation boundaries directly on images
  • Real-time metrics: See Dice score, Hausdorff distance, and pixel accuracy update as you draw
  • Visual overlays: Color-coded display shows agreement and disagreement with ground truth
  • Multiple examples: Practice with simple shapes and complex anatomical structures

Why Interactive Demos Matter

Segmentation metrics like Dice coefficient and Hausdorff distance are formulas until you see them in action. This tool makes metrics tangible—draw a boundary and instantly see how different metrics respond to your segmentation decisions.

Using the Tool

  1. Select an image with provided ground truth segmentation
  2. Draw your segmentation using mouse-based boundary tools
  3. Observe metrics updating in real-time as you draw
  4. Compare visually with color overlays showing errors
  5. Iterate to understand how boundary precision affects each metric

Technical Background

  • Dice Score: Measures overlap (2 A∩B /( A + B )). Values near 1 indicate good agreement.
  • Hausdorff Distance: Maximum distance between boundary points. Sensitive to outliers.
  • Pixel Accuracy: Fraction of correctly classified pixels. Can be misleading with class imbalance.

These metrics are standard for evaluating automated segmentation algorithms and serve as loss functions in deep learning.

Educational Value

Students gain:

  • Metric intuition: Understand what different measures actually evaluate
  • Practical skills: Experience manual segmentation challenges
  • Quality trade-offs: See speed versus accuracy in real-time
  • Clinical context: Learn acceptable quality standards for medical applications

This foundation connects to deep learning, where segmentation quality drives loss function design and model evaluation.

Repository & Resources

Complete source code, sample images, and educational materials available on GitHub and MATLAB File Exchange, enabling widespread adoption in educational settings.