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Wound Border Characterization Documentation!
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**Quantitative 3D Depth-Based Analysis and Machine Learning-Based Classification of Chronic
Wound Borders**

This project presents a fully automated computational pipeline for objective assessment and
classification of chronic wound borders using advanced 3D depth map analysis around wound borders. The system employs
machine learning techniques to extract quantitative features from wound geometries, enabling precise
characterization of wound border types that traditionally require subjective clinical evaluation.

Core Functionality
==================

* **Quantitative Feature Extraction:** Automated extraction of comprehensive geometric and morphological features from 3D wound depth data

* **Unsupervised Edge Type Discovery:** Pattern recognition algorithms to identify distinct wound border characteristics without prior labeling

* **Automated Classification:** Machine learning-based classification system for real-time wound border type determination

Usage Modes
===========

The system supports two operational configurations:
   
1. **Complete Training Pipeline:** Full workflow for developing custom classification models on user-specific datasets

2. **Standalone Classification Tool:** Ready-to-use application utilizing pre-trained models for immediate wound assessment

This solution bridges the gap between clinical wound assessment and quantitative analysis, providing
healthcare professionals with objective, reproducible tools for wound border characterization.

Contents
========

.. toctree::
   :maxdepth: 2
   
   api/modules


Indices and tables
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* :ref:`genindex`
* :ref:`modindex`