Image Processing for Biomedical Applications
( 25 Modules )

Module #1
Introduction to Image Processing
Overview of image processing, importance in biomedical applications, and course objectives

Module #2
Image Representation and Storage
Digital image fundamentals, image file formats, and compression techniques

Module #3
Image Acquisition and Preprocessing
Biomedical image acquisition modalities, preprocessing techniques, and noise reduction

Module #4
Image Enhancement and Restoration
Image enhancement techniques, filtering, and restoration methods

Module #5
Image Filtering and Convolution
Linear and nonlinear filtering techniques, convolution, and spatial domain filtering

Module #6
Frequency Domain Processing
Fourier transform, filtering in the frequency domain, and image filtering applications

Module #7
Image Segmentation Fundamentals
Introduction to image segmentation, importance in biomedical applications, and segmentation methods

Module #8
Thresholding and Edge Detection
Thresholding techniques, edge detection methods, and applications in biomedical imaging

Module #9
Region-Based Segmentation
Region growing, region splitting, and merging techniques for image segmentation

Module #10
Edge-Based Segmentation
Edge detection algorithms, edge linking, and edge-based segmentation methods

Module #11
Clustering and Classification
Clustering algorithms, classification techniques, and feature extraction methods

Module #12
Active Contours and Level Sets
Active contours, level sets, and applications in biomedical image segmentation

Module #13
Image Registration and Fusion
Image registration techniques, image fusion methods, and applications in biomedical imaging

Module #14
Feature Extraction and Analysis
Feature extraction techniques, feature selection methods, and feature analysis in biomedical imaging

Module #15
Object Recognition and Tracking
Object recognition techniques, object tracking methods, and applications in biomedical imaging

Module #16
Machine Learning for Biomedical Image Analysis
Introduction to machine learning, supervised and unsupervised learning, and deep learning for biomedical image analysis

Module #17
Image Analysis for Disease Diagnosis
Image analysis techniques for disease diagnosis, image-based biomarkers, and computer-aided diagnosis systems

Module #18
Image Analysis for Image-Guided Interventions
Image analysis techniques for image-guided interventions, surgical planning, and navigation systems

Module #19
Image Analysis for Personalized Medicine
Image analysis techniques for personalized medicine, patient-specific modeling, and precision medicine

Module #20
Image Analysis for Neuroimaging Applications
Image analysis techniques for neuroimaging applications, brain image analysis, and neurological disorder diagnosis

Module #21
Image Analysis for Cardiovascular Applications
Image analysis techniques for cardiovascular applications, cardiac image analysis, and cardiovascular disease diagnosis

Module #22
Image Analysis for Cancer Imaging Applications
Image analysis techniques for cancer imaging applications, tumor image analysis, and cancer diagnosis

Module #23
Image Analysis for Musculoskeletal Applications
Image analysis techniques for musculoskeletal applications, bone and joint image analysis, and musculoskeletal disorder diagnosis

Module #24
Image Analysis for Pulmonary Applications
Image analysis techniques for pulmonary applications, lung image analysis, and respiratory disease diagnosis

Module #25
Course Wrap-Up & Conclusion
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Image Processing for Biomedical Applications ( 25 Modules )

Image Processing for Biomedical Applications
( 25 Modules )