Python Picket Fence Analysis Toolkit | DICOM Generator + Automated QA Analysis

Build Your Own Picket Fence Analysis Software in Python

This toolkit includes everything needed to generate realistic synthetic DICOM EPID images, automatically analyze MLC positioning accuracy, and generate professional QA reports.

Perfect for medical physicists, residents, students, and researchers interested in medical image processing and radiation therapy quality assurance.

Included Files

✅ Synthetic DICOM Picket Fence Image Generator

• Generate realistic EPID DICOM images
• Adjustable number of pickets
• Configurable MLC leaf errors
• Noise simulation
• Pixel spacing customization
• DICOM metadata included

✅ Automated Picket Fence Analysis

Automatically performs:

• Picket detection
• Leaf pair identification
• Radiation field segmentation
• Leaf center localization
• Position error calculation
• Mean and maximum error calculations
• Pass / Fail evaluation
• User-defined tolerance settings

✅ HTML QA Report

Automatically generates a professional report including

• Summary statistics
• Mean leaf error
• Maximum leaf error
• Individual leaf pair measurements
• Pass / Fail status
• Error plots
• Overlay visualization
• Timestamp and analysis settings

Python Libraries

• NumPy
• OpenCV
• SciPy
• pydicom
• Matplotlib

Who Is This For?

• Medical Physicists
• Medical Physics Residents
• Radiation Oncology Researchers
• Graduate Students
• Clinical Engineers
• Python Developers interested in medical imaging

What You'll Learn

✔ How the Picket Fence test works

✔ DICOM image processing

✔ Automatic picket detection

✔ MLC leaf localization

✔ Position error calculations

✔ QA tolerance evaluation

✔ Automated report generation

✔ Practical Python programming for medical physics