Biomedical Image Analysis
( 25 Modules )

Module #1
Introduction to Biomedical Image Analysis
Overview of biomedical image analysis, importance, and applications

Module #2
Image Fundamentals
Basics of image representation, resolution, and file formats

Module #3
Image Acquisition Techniques
Introduce various biomedical imaging modalities (e.g., MRI, CT, X-ray, ultrasound)

Module #4
Image Preprocessing
Image filtering, normalization, and artifact removal techniques

Module #5
Image Enhancement
Contrast enhancement, noise reduction, and image sharpening techniques

Module #6
Image Segmentation Fundamentals
Introduction to image segmentation, thresholding, and edge detection

Module #7
Thresholding Techniques
Manual, automatic, and adaptive thresholding methods

Module #8
Edge Detection Techniques
Gradient-based, Laplacian-based, and Canny edge detection methods

Module #9
Region-Based Segmentation
Region growing, region splitting, and watershed transform

Module #10
Active Contour Models
Snakes, geodesic active contours, and level set methods

Module #11
Image Registration
Introduction to image registration, alignment, and warping

Module #12
Feature Extraction Techniques
Shape, texture, and intensity feature extraction methods

Module #13
Pattern Recognition and Classification
Introduction to classification, regression, and clustering

Module #14
Machine Learning for Biomedical Image Analysis
Supervised, unsupervised, and deep learning methods for biomedical image analysis

Module #15
Convolutional Neural Networks for Image Analysis
Introduction to CNNs, architectures, and applications

Module #16
Image Analysis for Disease Diagnosis
Applications of image analysis in disease diagnosis (e.g., cancer, cardiac, neurological)

Module #17
Image Analysis for Image-Guided Interventions
Applications of image analysis in image-guided interventions (e.g., surgical, radiation therapy)

Module #18
Evaluation Metrics and Validation
Quantitative evaluation metrics for image analysis and validation techniques

Module #19
Biomedical Image Analysis Software and Tools
Overview of popular software and tools for biomedical image analysis (e.g., OpenCV, ITK, 3D Slicer)

Module #20
Image Analysis for Personalized Medicine
Applications of image analysis in personalized medicine and genomics

Module #21
Image Analysis for Brain-Computer Interfaces
Applications of image analysis in brain-computer interfaces and neuroimaging

Module #22
Image Analysis for Cell and Tissue Engineering
Applications of image analysis in cell and tissue engineering and regenerative medicine

Module #23
Image Analysis for Medical Robotics
Applications of image analysis in medical robotics and computer-assisted surgery

Module #24
Image Analysis for Public Health and Epidemiology
Applications of image analysis in public health and epidemiology

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Biomedical Image Analysis career

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Biomedical Image Analysis ( 25 Modules )

Biomedical Image Analysis
( 25 Modules )