🔬 Retinal Blood Vessel Segmentation from Color Fundus Images

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Advanced retinal vessel segmentation framework with ensemble machine learning and multi-scale feature extraction

Machine Learning	Deep Learning	Ensemble Methods	Feature Engineering

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Benchmark Performance Results

Clinical-grade accuracy achieved through ensemble intelligence and hierarchical feature engineering

Dataset	Accuracy	Sensitivity	Specificity	AUC	Best Method	Processing Time
DRIVE	96.1%	78.5%	98.4%	88.9%	Ensemble	~15s/image
STARE	95.6%	80.1%	97.8%	89.2%	AdaBoost	~12s/image
CHASE_DB1	94.9%	77.8%	98.1%	87.8%	SVM-RBF	~18s/image
Comprehensive Performance Analysis
Average	95.5%	78.8%	98.1%	88.6%	Multi-Method	~15s/image

Performance Highlights	Technical Innovation	Clinical Applications
Best-in-Class Accuracy	Multi-Scale Features	Real-Time Processing
96.1% on DRIVE dataset	32→512-bit binary descriptors	<20s per fundus image
Ensemble Intelligence	Deep Learning Integration	Clinical Validation
Multi-classifier fusion	VGG-16 pre-trained features	Validated on 3 datasets

Results achieved with ensemble methods and hierarchical feature descriptors
Detailed performance analysis and ablation studies available in published papers
Processing times measured on Intel i7-8700K with 32GB RAM

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Key Technical Innovations

Multi-Dimensional Features 32/64/128/512-bit binary patterns Hierarchical Local Haar descriptors Saha adaptive thresholding variant Enhanced SURF keypoints

Machine Learning Methods Random Forest (50-500 trees) SVM (RBF/Linear kernels) AdaBoost ensemble Pre-trained CNN features

Ensemble Intelligence Majority voting Weighted voting Meta-learner stacking Confidence fusion

Production Features Optimized processing Comprehensive evaluation Python/MATLAB integration Complete documentation

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Project Overview

Clinical Impact: This repository provides a comprehensive, production-ready suite for automated retinal blood vessel segmentation from color fundus photographs. Our methods support computer-aided diagnosis of diabetic retinopathy, glaucoma, hypertensive retinopathy, and other cardiovascular diseases through precise vessel analysis.

Research Innovation: Featuring peer-reviewed implementations published in top-tier venues (IET, AIME, IbPRIA), this project explores cutting-edge combinations of supervised, unsupervised, and semi-supervised learning approaches with ensemble intelligence.

Advanced Machine Learning Arsenal

Supervised Learning	Unsupervised Methods	Ensemble Intelligence
Random Forest	Multi-scale line detection	Multi-Classifier Ensemble
Support Vector Machine	Adaptive thresholding	Weighted voting
AdaBoost Ensemble	Connected component analysis	Stacking meta-learning
Deep CNN Features	Morphological operations	Confidence aggregation

Technical Innovation Stack

• Multi-Dimensional Feature Descriptors: 32/64/128/512-bit binary patterns with hierarchical analysis
• Advanced Line Detection: Multi-scale oriented filters with standardization and noise reduction
• SURF-Enhanced Features: Modified keypoint detection with region-aware processing
• Ensemble Intelligence: Majority/weighted/stacking voting with uncertainty quantification
• Deep Learning Integration: VGG-based features and adaptive patch extraction
• High-Performance Computing: Optimized MATLAB implementation with Python extensions

Comprehensive Framework Architecture

This project implements a multi-tiered approach combining traditional computer vision with modern machine learning:

Core Methodological Approaches

Approach Category	Implementation	Key Features	Best Use Case
Supervised Learning	Random Forest, SVM, AdaBoost	Pixel-wise classification, ensemble voting	High-accuracy vessel detection
Unsupervised Methods	Multi-scale line detection	Orientation-aware filtering, adaptive thresholding	Real-time processing, no training data
Semi-Supervised	Hybrid labeled/unlabeled	Confidence-based learning, active sampling	Limited annotation scenarios
Ensemble Intelligence	Multi-classifier fusion	Weighted voting, stacking, uncertainty quantification	Maximum performance scenarios

Advanced Feature Engineering Pipeline

• Hierarchical Local Haar Patterns (LHP): 32→64→128→512-bit binary descriptors with multi-scale analysis
• Enhanced SURF Descriptors: Modified keypoint detection optimized for retinal vessel morphology
• Adaptive Binary Features: Saha variant with vessel-specific thresholding and boundary enhancement
• Deep Learning Features: VGG-based CNN features integrated with traditional descriptors
• Multi-Scale Line Detection: Oriented filters across 12 scales with standardization and noise reduction

Machine Learning Ensemble System

Individual Classifiers:

• Random Forest: 50-500 trees with balanced sampling and out-of-bag validation
• Support Vector Machine: RBF/Linear kernels with comprehensive feature standardization
• AdaBoost: Adaptive boosting with focus on difficult vessel pixels
• Deep Features: Pre-trained CNN integration for enhanced discrimination

Ensemble Combination Methods:

• Majority Voting: Democratic classifier combination
• Weighted Voting: Performance-based weight allocation with softmax normalization
• Stacking: Meta-classifier learning optimal combination strategies
• Confidence Fusion: Uncertainty-aware prediction aggregation

Post-Processing Intelligence

• Connected Component Analysis with adaptive size filtering
• Morphological Operations guided by ensemble confidence
• Vessel Continuity Enhancement using morphological reconstruction
• Noise Filtering with uncertainty-guided adaptive thresholds

Publications

Published Research

Year	Venue	Title	Type
2021		"An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods"	Journal
2019		"A semi-supervised approach to segment retinal blood vessels in color fundus photographs"	Conference
2019		"Retinal blood vessel segmentation: A semi-supervised approach"	Conference

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📖 Citations

🔗 Click to expand BibTeX citations

@article{sayed2021innovate,
  title={An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods},
  author={\textbf{Md Abu Sayed} and Saha, Sajib and Rahaman, GM Atiqur and Ghosh, Tanmai K and Kanagasingam, Yogesan},
  journal={IET Image Processing},
  volume={15},
  number={1},
  pages={180--190},
  year={2021},
  publisher={Wiley Online Library}
}

@inproceedings{sayed2019semi,
  title={A semi-supervised approach to segment retinal blood vessels in color fundus photographs},
  author={\textbf{Md Abu Sayed} and Saha, Sajib and Rahaman, GM and Ghosh, Tanmai K and Kanagasingam, Yogesan},
  booktitle={Conference on Artificial Intelligence in Medicine in Europe},
  pages={347--351},
  year={2019},
  organization={Springer}
}

@inproceedings{ghosh2019retinal,
  title={Retinal blood vessel segmentation: A semi-supervised approach},
  author={Ghosh, Tanmai K and Saha, Sajib and Rahaman, GM and \textbf{Md Abu Sayed} and Kanagasingam, Yogesan},
  booktitle={Iberian Conference on Pattern Recognition and Image Analysis},
  pages={98--107},
  year={2019},
  organization={Springer}
}

📝 Citation Notice: Please cite the relevant papers when using this code in your research.

Project Structure

Click to view complete project structure

retinalVesselSegmentation/
├── README.md                       # Project documentation
├── accuracy_tesst.m                # Accuracy evaluation metrics
├── setupProject.m                  # Automated project setup
├── src/                            # Source code directory
│   ├── classification/             # Machine learning classifiers
│   │   ├── trainRFC.m             # Random Forest training
│   │   ├── testRFC.m              # RF model testing
│   │   ├── trainSVM.m             # SVM training
│   │   ├── testSVM.m              # SVM testing
│   │   ├── trainAdaBoost.m        # AdaBoost training
│   │   ├── testAdaBoost.m         # AdaBoost testing
│   │   ├── trainEnsemble.m        # Ensemble training
│   │   └── testEnsemble.m         # Ensemble testing
│   ├── core/                      # Core segmentation functions
│   │   ├── VesselSegment.m        # Main segmentation function
│   │   ├── im_seg.m               # Image segmentation core
│   │   ├── multi_test.m           # Multi-scale wrapper
│   │   ├── get_lineresponse.m     # Line filter response
│   │   └── get_linemask.m         # Line mask generation
│   ├── features/                  # Feature extraction methods
│   │   ├── extractFeature.m       # SURF feature extraction
│   │   ├── extractFeatureH.m      # Hierarchical features
│   │   ├── create_binary.m        # 16-bit binary features
│   │   ├── create_binary_32.m     # 32-bit binary features
│   │   ├── create_binary_64.m     # 64-bit binary features
│   │   ├── create_binary_128.m    # 128-bit binary features
│   │   ├── create_binary_512.m    # 512-bit binary features
│   │   ├── create_binary_saha.m   # Saha variant features
│   │   └── OpenSurf_Sheen.m       # Modified SURF implementation
│   ├── preprocessing/             # Image preprocessing
│   │   ├── standardize.m          # Image standardization
│   │   ├── noisefiltering.m       # Noise removal
│   │   └── fakepad.m              # Image padding
│   ├── python/                    # Python integration
│   │   ├── patch_extraction.py    # Adaptive patch extraction
│   │   └── vgg_feature.py         # Deep learning features
│   └── evaluation/                # Performance evaluation
│       └── accuracy_tesst.m       # Metrics calculation
├── Images/                        # Dataset images and results
│   └── RFC SET/
│       ├── DRIVE/                 # DRIVE dataset
│       ├── STARE/                 # STARE dataset
│       └── CHASEDB1/              # CHASE_DB1 dataset
├── docs/                          # Documentation
│   ├── INSTALLATION.md            # Setup guide
│   ├── USAGE.md                   # Usage examples
│   └── API.md                     # API reference
└── Publications/                  # Research papers

Methodology

Click to expand methodology details

1. Multi-Scale Line Detection

The algorithm employs multi-scale line detectors to enhance vessel structures:

• Uses oriented line masks at different scales (1, 3, 5, ..., W)
• Combines responses across multiple orientations (0°, 15°, 30°, ..., 165°)
• Applies standardization and noise reduction

2. Feature Extraction

Two main feature extraction approaches:

Hierarchical Patch Descriptors with Local Haar Patterns (LHP)

• Extracts 16-512 binary features from image patches
• Uses integral images for efficient computation
• Hierarchical decomposition for multi-resolution analysis

SURF-based Features

• Modified SURF descriptor extraction
• Region of interest (ROI) aware feature detection
• 64-dimensional feature vectors

3. Classification

Multiple supervised learning approaches were implemented and compared:

• Random Forest Classifier with 50-500 trees (primary approach)
• Support Vector Machine (SVM) with RBF/Linear kernels for comparative analysis
• AdaBoost ensemble learning for enhanced weak learner performance
• Training on vessel vs. non-vessel pixels
• Balanced sampling (60% vessel, 40% non-vessel)
• Cross-validation and out-of-bag validation for performance estimation

4. Post-processing

• Connected component analysis
• Noise filtering (removes objects < 100 pixels)
• Binary vessel segmentation output

Supported Datasets

Standard Retinal Datasets

Digital Retinal Images for Vessel Extraction High-resolution fundus images Gold standard annotations Widely used benchmark

STructured Analysis of the Retina Pathological cases included Multiple annotators Challenging dataset

Child Heart & Health Study Pediatric images High detail annotations Unique characteristics

Documentation

Comprehensive Guides and References

Guide	Description	Link
Installation	Setup guide with prerequisites and dependencies	docs/INSTALLATION.md
Usage Guide	Detailed examples and workflows	docs/USAGE.md
API Reference	Complete function documentation	docs/API.md
Contributing	Guidelines for contributors	CONTRIBUTING.md
License	Usage terms and citations	LICENSE
Changelog	Version history and updates	CHANGELOG.md

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Quick Start

Get Started in 3 Easy Steps

Step 1: Prerequisites % Required MATLAB toolboxes: % - MATLAB R2016b+ % - Image Processing Toolbox % - Statistics & ML Toolbox

Step 2: Training % Run training script trainRFC % Choose mode: % 1 - Pre-extracted features % 2 - Extract from images

Step 3: Testing % Test all datasets testRFC % Or single image img = imread('image.jpg'); mask = imread('mask.png'); result = VesselSegment(img, mask);

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Evaluation

% Calculate performance metrics
accuracy_tesst

Computed Metrics:

• Accuracy - Overall classification performance
• Sensitivity - True Positive Rate (vessel detection)
• Specificity - True Negative Rate (background detection)
• AUC - Area Under Curve approximation

Performance Results

The method achieves competitive performance on standard datasets:

Dataset	Accuracy	Sensitivity	Specificity	AUC
DRIVE	~95.2%	~75.8%	~98.1%	~86.9%
STARE	~94.8%	~78.2%	~97.6%	~87.9%
CHASE_DB1	~94.1%	~76.4%	~97.8%	~87.1%

Results may vary based on training configuration and dataset preprocessing

Key Functions

Core API Reference

Core Segmentation VesselSegment(img, mask) % Main function im_seg(img, mask, W) % Multi-scale detection get_lineresponse(img, W, L) % Line filter response Feature Extraction extractFeatureH(img, segImg, mask) % Hierarchical features create_descriptor(img, mask, patchSize) % Patch descriptors create_binary(r, c, integralImg, ...) % Binary features

Machine Learning trainRFC.m % Random Forest training pipeline testRFC.m % Multi-classifier testing (RF/SVM/AdaBoost) % Additional classifiers for comparative analysis: % - Support Vector Machine (SVM) % - AdaBoost ensemble learning Configuration W = 15; % Window size for line detection patchSize = 32; % Patch size for features numTrees = 50; % Number of trees in RF noiseSize = 100; % Noise filtering threshold

Customization

Advanced Configuration Options

Parameter Tuning

% Window size for line detection (typically 15)
W = 15;

% Patch size for feature extraction (16 or 32)
patchSize = 32;

% Number of trees in Random Forest
numTrees = 50;

% Noise filtering threshold
noiseSize = 100;

Adding New Datasets

1. Create folder structure:

   Images/RFC SET/[DATASET_NAME]/
   ├── train/
   ├── test/
   ├── multiscale_mask/
   └── rfc_mask/
   

2. Update configuration:

   • Modify dataset list in trainRFC.m
   • Update extensions in testRFC.m
   • Add appropriate file patterns

Authors

Research Team

Primary Implementation & Research	Co-author & Contributor
Co-author & Contributor	Co-author & Contributor
Senior Author & Supervisor

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🤝 Contributing

We welcome contributions from the research community!

🌟 How to Contribute:

• 🐛 Report bugs or issues
• 💡 Suggest improvements and new features
• 🔄 Submit pull requests with enhancements
• 📊 Share results on new datasets
• 📚 Improve documentation

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🙏 Acknowledgments

Special thanks to the research community

• 🗃️ Public Datasets: DRIVE, STARE, CHASE_DB1 research communities
• 🌊 OpenSURF: Implementation by Chris Evans
• 🔧 MATLAB Community: Various utility functions and support

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📞 Contact

💬 Get in Touch

For questions, collaborations, or research discussions

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📄 License

This project is released for academic and research purposes
Please cite the relevant papers when using this code

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⭐ If this work helps your research, please consider giving it a star! ⭐

Last updated: February 2026