BMEG-E 511 Biomedical Image Processing
3 credits
- Prerequisite(s): None
- Delivery: On-Campus
Description
This course covers biomedical image processing principles and applications, including image analysis, enhancement, segmentation, registration, and 3D reconstruction. Advanced methods like machine learning are introduced to address modern challenges. Hands-on exercises reinforce skills, enabling students to apply image processing techniques in clinical and research contexts for biomedical imaging.
Topics
Introduction to biomedical imaging
- Biomedical imaging modalities
- Digital image representation
- Applications of image processing
Intensity transformations
- Image histograms
- Contrast enhancement techniques
- Thresholding methods
Spatial filtering
- Smoothing techniques
- Sharpening methods
Frequency domain operations
- Fourier transform and its properties
- Frequency-based filtering
Image enhancement and restoration
- Noise sources and types
- Noise removal techniques
- Image restoration basics
Image segmentation
- Thresholding techniques
- Edge detection methods
- Region-based segmentation
Advanced segmentation
- Active contour models
- Graph-based segmentation
Image registration
- Rigid and non-rigid transformations
- Optimization methods
- Multimodal alignment
3D reconstruction and visualization
- Volume representation
- Rendering techniques
Machine learning
- Feature extraction and classification
- Supervised learning techniques
Deep learning
- Convolutional neural networks
- Biomedical imaging applications
Image-guided therapy
- Real-time image processing
- Navigation and guidance systems
Multimodal imaging and data fusion
- Combining imaging modalities
- Applications in diagnostics
Challenges and future directions
- Limitations of current techniques
- Emerging trends in imaging technologies
Learning Outcomes
- Analyze biomedical images to extract meaningful information using intensity transformations, filtering techniques, and segmentation methods.
- Evaluate the effectiveness of spatial and frequency domain operations in enhancing image quality for clinical and research applications.
- Apply advanced image registration techniques to align multimodal medical images with precision.
- Design and implement algorithms for 3D reconstruction and visualization of biomedical datasets.
- Create innovative solutions to contemporary biomedical imaging challenges using advanced methods such as machine learning and data fusion.
- Assess the limitations and advantages of various biomedical image processing techniques for different imaging modalities.
- Develop a comprehensive project that integrates multiple image processing techniques to address a real-world biomedical problem.
- Synthesize knowledge of biomedical imaging methods to propose improvements in image-guided therapy and real-time processing applications.
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.