Biomedical Signal and Image Processing
All of the biomedical measurement technologies, which are now instrumental to the medical field, are essentially useless without proper signal and image processing. Biomedical Signal and Image Processing is unique in providing a comprehensive survey of all the conventional and advanced imaging modalities and the main computational methods used for processing the data obtained from each.
This book offers self-contained coverage of the mathematics and biology/physiology necessary to build effective algorithms and programs for biomedical signal and image processing applications. The first part of the book details the main signal and image processing, pattern recognition, and feature extraction techniques along with computational methods from other fields such as information theory and stochastic processes. Building on this foundation, the second part explores the major one-dimensional biological signals, the biological origin and importance of each signal, and the commonly used processing techniques with an emphasis on physiology and diagnostic applications, while the third section does the same for imaging modalities.
Throughout the book, the authors rely on practical examples using real data from biomedical systems. They supply several programming examples in MATLABŪ to provide hands-on experience and insight
Integrating all major modalities and computational techniques in a single source, Biomedical Signal and Image Processing is a perfect introduction to the field as well as an ideal reference for the established professional.
Signals and Biomedical Signal Processing
Image Filtering Enhancement and Restoration
Electrical Activities of Cell
Other editions - View all
action potential algorithm analysis applications basis functions beam biological tissues biomedical image biomedical signal blood brain Butterworth filter calculate captured chapter cluster coefficients complex components compression computed decomposition defined depolarization described detectors diagnostics discrete signal DISCRETE WAVELET TRANSFORM discussed disease edge detection EEG signal electrodes Equation example fMRI Fourier transform frequency domain Gaussian gray levels heart highpass filters identify image processing image registration impulse input ions linear lowpass filter magnetic field mask MATLAB measure membrane methods microscopy mother wavelet motor unit neural networks neurons noise original image perceptron PET imaging photon pixels points problem pulse recording region result sample scans shown in Figure shows signal and image signal processing specific STFT stochastic process subimages techniques thresholding tumor types typical ultrasound imaging ventricle ventricular wave wavelet transform x-ray