Medical Image Registration with MATLAB: Techniques, Applications, and Clinical Implementation

Master image registration techniques for clinical impact

Medical image registration is the backbone of modern diagnostics, surgery planning, and therapeutic interventions. Learn the science and application of aligning medical images to support clinical decisions on this four-week course.

Whether you’re a biomedical engineer, clinician, or researcher, this course helps bridge the gap between theory and practice. You’ll explore core methods, software, and real-world case studies that are critical for effective implementation in clinical settings.

Discover the foundations of image registration in healthcare

Start by understanding the basics: what image registration is, why it matters in modern healthcare, and how it supports diagnostics and treatment.

You’ll explore different imaging modalities such as MRI, CT, PET, and ultrasound, and learn key principles of medical image analysis.

Implement core algorithms and techniques with MATLAB

Build hands-on skills in implementing image registration and alignment algorithms, including intensity-based, feature-based, and geometric transformations.

Using tools like MATLAB and 3D Slicer, you’ll apply techniques like rigid, affine, and deformable transformations and explore how to handle real-world implementation challenges.

Advance your skills with multimodal and 3D registration

Go deeper with advanced methods such as mutual information, gradient descent optimisation, and multimodal image alignment.

You’ll also explore validation metrics, file formats, coordinate systems, and the use of AI to improve accuracy and efficiency. Practical case studies in clinical imaging—spanning neurology, cardiology, oncology, and dentistry—bring theory to life.

This course is ideal for biomedical engineers, clinicians, radiologists, medical imaging specialists, and researchers looking to gain practical skills in medical image registration.

This course is ideal for biomedical engineers, clinicians, radiologists, medical imaging specialists, and researchers looking to gain practical skills in medical image registration.

• Apply MATLAB to design and implement image registration workflows for real-world monomodal and multimodal medical imaging tasks.
• Assess image registration alignment using visualization and similarity measures such as Mutual Information (MI), Correlation Coefficient (CC), and Mean Squared Error (MSE).
• Develop robust registration solutions by incorporating transformations, optimization techniques, and advanced strategies like multiresolution approach and parameter normalization.