Circular particle detection using sectored ring mask for synchrotron PCXI images.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Pulmonology

Background:

• Cystic Fibrosis (CF) impairs respiratory function and mucociliary transit (MCT).
• Accurate MCT measurement is crucial for assessing CF treatment efficacy.
• Existing particle detection methods struggle with small, non-circular, and overlapping particles in noisy imaging data.

Purpose of the Study:

• To develop a robust particle detection method for analyzing MCT in CF patients.
• To improve the accuracy and automation of image analysis for respiratory treatments.

Main Methods:

• Utilized Synchrotron X-ray imaging to visualize small particles in the respiratory system.
• Developed a novel detection method using sectored ring masks (SRM) and gradient descent.
• Implemented edge extraction, gradient analysis, and circle fitting to refine particle identification.

Main Results:

• Achieved high detection rates with 91.9% precision and 89.0% recall.
• Demonstrated robust performance in identifying small, potentially overlapping particles.
• Validated the method through experimental studies.

Conclusions:

• The proposed SRM-based method offers effective and reliable particle detection for CF research.
• This technique facilitates objective assessment of mucociliary transit and treatment progress.
• Enables automated analysis of respiratory system particle dynamics.