Biosignal and Medical Image Processing (Signal Processing and Communications, 22)
Relying heavily on MATLAB® problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications, providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts. The first and only textbook to supply a hands-on tutorial in biomedical signal and image processing, it offers a unique and proven approach to signal processing instruction, unlike any other competing source on the topic. The text is accompanied by a CD with support data files and software including all MATLAB examples and figures found in the text.
Why Read This Book
You will get a hands-on, MATLAB-driven introduction to real biomedical signal and image problems, with runnable examples and data so you can reproduce and extend the analyses. The book emphasizes practical implementation, common pitfalls, and worked case studies (ECG, EEG, medical images) that make algorithms immediately useful in engineering projects.
Who Will Benefit
Graduate students, engineers, and researchers working on biomedical signal/image analysis who want practical, MATLAB-based recipes for preprocessing, spectral analysis, filtering, wavelets, and basic image processing.
Level: Intermediate — Prerequisites: Basic signals and systems, undergraduate-level calculus and linear algebra, and familiarity with MATLAB (script writing and basic plotting).
Key Takeaways
- Implement common biosignal preprocessing and filtering workflows in MATLAB (baseline removal, notch filtering, artifact rejection).
- Perform time- and frequency-domain analyses of physiological signals (spectral estimation, PSD, short-time Fourier analysis).
- Apply wavelet-based denoising and time-frequency techniques to nonstationary biosignals.
- Process and enhance medical images using MATLAB (contrast enhancement, filtering, restoration, basic segmentation).
- Extract features from signals and images for measurement or simple classification tasks (peak detection, HRV metrics, image features).
Topics Covered
- Introduction to Biomedical Signals and Images
- MATLAB Essentials and Data Handling (examples and scripts)
- Characteristics of Physiological Signals (ECG, EEG, EMG, respiration)
- Time-Domain Analysis and Preprocessing
- Frequency-Domain and Spectral Analysis
- Digital Filtering Techniques for Biosignals
- Wavelets and Time-Frequency Methods in Biomedical Signals
- Statistical and Ensemble Methods for Signal Averaging
- Noise Reduction and Artifact Removal
- Medical Image Fundamentals and Representation
- Image Enhancement, Restoration, and Filtering
- Image Segmentation and Feature Extraction
- Case Studies and Application Examples (ECG, EEG, imaging)
- Appendices: MATLAB Code and Data Files
Languages, Platforms & Tools
How It Compares
Compared with Rangayyan's 'Biomedical Signal Analysis' (more theoretical and comprehensive), Semmlow is more of a hands-on, MATLAB-implementation text; compared to general DSP texts it focuses narrowly on biomedical examples and practical workflows rather than deep theory.
