From a former student: "I liked this class so much that I was overjoyed to find out that I would need a CT scan when I broke my arm. Fortunately, they gave me the raw data on a CD in the form of 2D slices. So I quickly searched for my Lab 3 and processed it into a 3D model in MATLAB. It came out pretty well!" (Courtesy of Jonathan A. Cox. Used with permission.)
Special software is required to use some of the files in this course: .m, .mat, .dat, .zip, and .gz files.
Syllabus
Overview
This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done on the MIT Server in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.
Prerequisites
6.003 Signals and Systems, 2.004 Dynamics and Control II, 16.004 Unified Engineering IV, or 18.085 Computational Science and Engineering I.
Lecture Topics
 Biomedical Signals and Images
 ECG: Cardiac electrophysiology, relation of electrocardiogram (ECG) components to cardiac events, clinical applications. Guest lecture.
 Speech Signals: The sourcefilter model of speech production, spectrographic analysis of speech.
 Speech Coding: Analysissynthesis systems, channel vocoders, linear prediction of speech, linear prediction vocoders.
 Imaging Modalities: Survey of major modalities for medical imaging: ultrasound, Xray, CT, MRI, PET, and SPECT.
 MRI: Physics and signal processing for magnetic resonance imaging. Guest lecture.
 Surgical Applications: A survey of surgical applications of medical image processing. Guest lecture.
 Fundamentals of Deterministic Signal and Image Processing
 Data Acquisition: Sampling in time, aliasing, interpolation, and quantization.
 Digital Filtering: Difference equations, FIR and IIR filters, basic properties of discretetime systems, convolution.
 DTFT: The discretetime Fourier transform and its properties. FIR filter design using windows.
 DFT: The discrete Fourier transform and its properties, the fast Fourier transform (FFT), the overlapsave algorithm, digital filtering of continuoustime signals.
 Sampling Revisited: Sampling and aliasing in time and frequency, spectral analysis.
 Image processing I: Extension of filtering and Fourier methods to 2D signals and systems.
 Image processing II: Interpolation, noise reduction methods, edge detection, homomorphic filtering.
 Probability and Random Signals
 PDFs: Introduction to random variables and probability density functions (PDFs).
 Classification: Bayes' rule, detection, statistical classification.
 Estimating PDFs: Practical techniques for estimating PDFs from real data.
 Random signals I: Time averages, ensemble averages, autocorrelation functions, crosscorrelation functions.
 Random signals II: Random signals and linear systems, power spectra, cross spectra, Wiener filters.
 Blind source separation: Use of principal component analysis (PCA) and independent component analysis (ICA) for filtering.
 Image Segmentation and Registration
 Image Segmentation: statistical classification, morphological operators, connected components.
 Image Registration I: Rigid and nonrigid transformations, objective functions.
 Image Registration II: Joint entropy, optimization methods.
Laboratory Projects
Optional: Fundamentals of MATLAB®
Optional introduction/review of software package used throughout the semester. (1 week  Siracusa)
 ECG Filtering and Frequency Analysis of the Electrogram
Design filters to remove noise from electrocardiogram (ECG) signals and then design a system to detect lifethreatening ventricular arrhythmias. The detector is tested on normal and abnormal ECG signals. (3 weeks  Greenberg)
 Speech Coding
Implement, test, and compare two speech analysissynthesis systems. These systems utilize a pitch detector and a speech synthesizer based on the sourcefilter model of speech production. (3 weeks  Greenberg)
 Image Segmentation
Process clinical MRI scans of the human brain to reduce noise, label tissue types, extract brain contours, and visualize 3D anatomical structures. (2 weeks  Fisher)
 Image Registration
Explore the coregistration of medical images, focusing on 2D to 2D (slice to slice) registration and using nonlinear optimization methods to maximize various measures of image alignment. (2 weeks  Fisher)
 ECG: Blind Source Separation
Separate fetal and maternal ECG signals using techniques based on second and higherorder statistical methods. Techniques include Wiener filtering, principal component analysis, and independent component analysis. (2 weeks  Clifford/Greenberg)
Bibliography
General
Oppenheim, A. V., and R. W. Schafer, with J. R. Buck. DiscreteTime Signal Processing. 2nd ed. Upper Saddle River, NJ: PrenticeHall, 1999. ISBN: 9780137549207.
Papoulis, A., and S. U. Pillai. Probability, Random Variables, and Stochastic Processes. New York, NY: McGraw Hill, 2001. ISBN: 9780072817256.
Basics
Siebert, W. M. Circuits, Signals and Systems. Cambridge, MA: MIT Press, 1985. ISBN: 9780262192293.
Oppenheim, A. V., and A. S. Willsky, with H. Nawab. Signals and Systems. 2nd ed. Upper Saddle River: PrenticeHall, 1996. ISBN: 9780138147570.
Karu, Z. Z. Signals and Systems Made Ridiculously Simple. Huntsville, AL: ZiZi Press, 1995. ISBN: 9780964375215.
Probability and Classification
Duda, R., and P. Hart. Pattern Classification and Scene Analysis. New York, NY: John Wiley & Sons, 1973. ISBN: 9780471223610.
Duda, R., P. Hart, and D. Stork. Pattern Classification. 2nd ed. New York, NY: John Wiley & Sons, 2000. ISBN: 9780471056690.
Bishop, C. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1996. ISBN: 9780198538646.
Nabney, I. Netlab: Algorithms for Pattern Recognition. 3rd ed. New York, NY: Springer, 2004. ISBN: 9781852334406.
ECG Analysis
Clifford, G., F. Azuajae, and P. McSharry. Advanced Methods and Tools for ECG Data Analysis. Norwood, MA: Artech House, 2006. ISBN: 9871580539661.
Speech Analysis
Rabiner, L. R., and R. W. Schafer. Digital Processing of Speech Signals. Upper Saddle River, NJ: PrenticeHall, 1978. ISBN: 9780132136037.
Quatieri, T. F. DiscreteTime Speech Signal Processing: Principles and Practice. Upper Saddle River, NJ: PrenticeHall, 2001. ISBN: 9780132429429.
Image Processing and Medical Imaging
Lim, J. S. TwoDimensional Signal and Image Processing. Upper Saddle River, NJ: Prentice Hall, 1989. ISBN: 9780139353222.
Gonzalez, R., and R. E. Woods. Digital Image Processing. 2nd ed. Upper Saddle River, NJ: PrenticeHall, 2002. ISBN: 9780201180756.
Epstein, C. L. Mathematics of Medical Imaging. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 9780130675484.
Webb, S. The Physics of Medical Imaging. New York, NY: Taylor & Francis, 1988. ISBN: 9780852743492.
Westbrook, C., C. Kaut Roth, and T. Talbot. MRI in Practice. 3rd ed. Malden, MA: Blackwell Science, Inc., 2005. ISBN: 9781405127875.
Macovski, A. Medical Imaging Systems. Upper Saddle River, NJ: Prentice Hall, 1983. ISBN: 9780135726853.
Grading
Grading criteria.

ACTIVITIES

PERCENTAGES

Lab reports (5 total)

60%

Quizzes (2 total)

25%

Problem sets (5 total)

10%

Class participation

5%

Problem sets are graded on a 04 scale, as follows:
Grading points.

GRADING POINTS

CRITERIA

4

Problem set contains few to no errors, indicating a thorough understanding of the material

3

Problem set contains some errors, indicating a lessthanthorough understanding of the material

2

Problem set is complete, but numerous errors indicate a lack of understanding of the material

1

Problem set is incomplete

0

Problem set not handed in, or is handed in late without prior arrangement

Recommended Citation
For any use or distribution of these materials, please cite as follows:
Julie Greenberg, William Wells, John Fisher, and Gari Clifford. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare (http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Calendar
Course calendar.
SES # 
TOPICS 
KEY DATES 
1 
Lecture 1: data acquisition 

2 
Lecture 2: digital filtering 
Problem set 1 out 
3 
Lab 0: fundamentals of MATLAB® 

4 
Lecture 3: ECG 
Lab 1 out 
5 
Lecture 4: DTFT 
Problem set 1 due
Problem set 2 out

6 
Lab 1A: ECG 

7 
Lecture 5: DFT 
Problem set 2 due
Problem set 3 out

8 
Lab 1B: ECG 

9 
Lecture 6: sampling revisited 

10 
Lecture 7: speech signals 
Problem set 3 due 
11 
Lab 1C: ECG 

12 
Lecture 8: speech coding 
Lab 2 out 
13 
Lecture 9: image processing I 
Lab 1 due
Problem set X out

14 
Lab 2A: speech coding 

15 
Lecture 10: PDFs 

16 
Lecture 11: classification 
Problem set X solutions out 
17 
Lab 2B: speech coding 

18 
Quiz I 

19 
Lecture 12: image processing II 

20 
Lab 2C: speech coding 

21 
Lecture 13: estimating PDFs 
Lab 3 out 
22 
Lecture 14: segmentation 
Lab 2 due
Problem set 4 out

23 
Lab 3A: image segmentation 

24 
Lecture 15: image registration I 

25 
Lecture 16: image registration II 
Problem set 4 due 
26 
Lab 3B: image segmentation 
Lab 4 out 
27 
Lecture 17: imaging modalities 
Problem set 5 out 
28 
Lab 4A: image registration 

29 
Lecture 18: random signals I 
Lab 3 due 
30 
Lecture 19: random signals II 
Problem set 5 due 
31 
Lab 4B: image registration 

32 
Lecture 20: blind source separation 
Lab 5 out 
33 
Lecture 21: MRI 
Lab 4 due
Problem set Y out

34 
Lab 5A: blind source separation 

35 
Lecture 22: surgical applications 
Problem set Y solutions out 
36 
Quiz 2 

37 
Lab 5B: blind source separation 

38 
Lecture 23: random signals III 

39 
Lecture 24: endofterm wrapup 
Lab 5 due 