BIOMEDICAL SIGNAL AND IMAGE PROCESSING

Transcription

1 BIOMEDICAL SIGNAL AND IMAGE PROCESSING EE SYLLABUS Instructor: Wei Qian, Ph.D. Professor of Electrical and Computer Engineering Medical Signal and Image Computerized Processing Scheme for Medical Signal and Image

2 Instructor: Wei Qian, Ph.D. Professor Department of Electrical and Computer Engineering Director: Medical Imaging Informatics Lab. College of Engineering, University of Texas, El Paso PH: (915) Office Hours: Monday/Wednesday: 2:30pm 6:00pm Any other time; both walking in and appointment to my office are WELCOME Prerequisites: This course requires the desire to learn, enjoy challenges, ability to work in a team, curiosity, and knowledge of basic mathematics, physics, or consent of the instructor. Course Objectives: This course prepares students from different disciplines for a career in biomedical signaling and imaging with corresponding biomedical signal and image analysis and processing. It provides a solid foundation in advanced biomedical signaling and imaging systems including up-to-date coverage of commercially relevant topics. It develops a range of skills that are highly sought after by employers. It also prepares students for research in biomedical signal and image computing and its clinical applications. Signaling and Imaging are central to biomedical engineering research. Course Description: Biomedical Signal and Image Processing (3-0) course will be focused on 3 B, the Basic theories, Basic techniques and Basic applications for 1: how to create biomedical signals, process the signals, and validate the methods and results for optimization of clinical applications, 2: how to create biomedical images, process the images, and validate the methods and results for optimization of clinical applications, and 3: how to entail the creation and advancement of 1-D (signal), 2-D (image) and 3-D (volume) databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data (bioinformatics issues).

3 This course provides a comprehensive survey of all the conventional and advanced signaling and imaging modalities and the main computational methods used for processing the data obtained from each. This course has the following Features: Presents a comprehensive treatment of all novel and conventional processing techniques along with the principles and applications of biomedical measurement technologies Emphasizes hands-on learning through examples using real biomedical data and programming examples in MATLAB Surveys the main computational methods associated with each one-dimensional signal and each imaging modality Discusses the basic physiology, biology, chemistry, and physics concepts related to measuring biomedical systems Course Contents Introduction to Digital Signal and Image Processing Signals and Biomedical Signal Processing What is a "Signal"? Analog, Discrete, and Digital Signals Processing and Transformation of Signals Signal Processing for Feature Extraction Some Characteristics of Digital Images Fourier Transform One-Dimensional Continuous Fourier Transform Sampling and NYQUIST Rate One-Dimensional Discrete Fourier Transform Two-Dimensional Discrete Fourier Transform Filter Design Image Filtering, Enhancement, and Restoration Point Processing Mask Processing: Linear Filtering in the Space Domain Frequency Domain Filtering Edge Detection and Segmentation of Images Edge Detection Image Segmentation Wavelet Transform From Fourier Transform to Short-Time Fourier Transform One-Dimensional Continuous Wavelet Transform One-Dimensional Discrete Wavelet Transform Two-Dimensional Wavelet Transform Main Applications of the DWT

4 Discrete Wavelet Transform in MATLAB Other Signal and Image Processing Methods Complexity Analysis Cosine Transform Introduction to Stochastic Processes Introduction to Information Theory Registration of Images Clustering and Classification Clustering versus Classification Feature Extraction K-Means: A Simple Clustering Method Bayesian Classifier Maximum Likelihood Method Neural Networks Processing of Biomedical Signals Electrical Activities of Cell Ion Transport in Biological Cells Electrical Characteristics of Cell Membranes Hodgkin-Huxley Model Electrical Data Acquisition Some Practical Considerations for Biomedical Electrodes Electrocardiogram Function and Structure of the Heart Electrocardiogram: Signal of the Cardiovascular System Cardiovascular Diseases and the ECG Processing and Feature Extraction of ECGs Electroencephalogram The Brain and Its Functions Electroencephalogram: Signal of the Brain Evoked Potentials Diseases of the Central Nervous System and the EEG EEG for Assessment of Anesthesia Processing and Feature Extraction of EEGs Electromyogram Muscle EMG: Signal Muscles Neuromuscular Diseases and the EMG Other Applications of the EMG Processing and Feature Extraction of the EMG Other Biomedical Signals Blood Pressure and Blood Flow Electrooculogram Magnetoencephaogram Respiratory Signals

5 More Biomedical Signals Processing of Biomedical Images Principles of Computed Tomography Formulation of Attenuation Computed Tomography The Fourier Slice Theorem X-Ray Imaging and Computed Tomography Introduction and Overview Physics of X-Rays Attenuation-Based X-Ray Imaging Image Quality Computed Tomography Biomedical CT Scanners Diagnostic Applications of X-Ray Imaging CT Images for Stereotactic Surgeries CT Registration for Other Image-Guided Interventions Complications of X-Ray Imaging Magnetic Resonance Imaging Physical and Physiological Principles of MRI MRI Formulation of MRI Reconstruction Functional MRI (fmri) Applications of MRI and fmri Processing and Feature Extraction of MRI Comparison of MRI with Other Imaging Modalities Registration with MR Images Ultrasound Imaging Why Ultrasound Imaging? Generation and Detection of Ultrasound Waves Physical and Physiological Principles of Ultrasound Resolution of Ultrasound Imaging Systems Ultrasound Imaging Modalities Modes of Ultrasound Image Representation Ultrasound Image Artifacts Three-Dimensional Ultrasound Image Reconstruction Applications of Ultrasound Imaging Processing and Feature Extraction of Ultrasonic Images Image Registration Comparison of CT, MRI, and Ultrasonic Images Bio-Effects of Ultrasound Positron Emission Tomography Physical and Physiological Principles of PET PET Signal Acquisition PET Image Formation Significance of PET

6 Applications of PET Processing and Feature Extraction of PET Images Comparison of CT, MRI, Ultrasonic, and PET Images Other Biomedical Imaging Techniques Optical Microscopy Fluorescent Microscopy Confocal Microscopy Near-Field Scanning Optical Microscopy Electrical Impedance Imaging Electron Microscopy Biometrics Teaching Approach The students will attend two lectures or laboratory/recitation session per week. There will be a homework assignment or project covering each class. In addition, there will be midterm exam or equivalent, and, of course, there will be final exam or equivalent. Attendance Policy Attendance is required for lectures and laboratory sessions. Textbook Biomedical Signal and Image Processing By Kayvan Najarian, Robert Splinter Published by CRC Press, 2006, ISBN , pages Grading & Evaluation The course grade will be determined by homework (40%), and one midterm examination or equivalent (30% total), and final examination or equivalent (30%).