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 Biomedical Signal and Image Processing  posted by  member150_php   on 2/22/2009  Add Courseware to favorites Add To Favorites  
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Abstract/Syllabus:

Clifford, Gari, John Fisher, Julie Greenberg, and William Wells, HST.582J Biomedical Signal and Image Processing, Spring 2007. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu (Accessed 07 Jul, 2010). License: Creative Commons BY-NC-SA

Biomedical Signal and Image Processing

Spring 2007

3D rendered image of armbones.
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.)

Course Description

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 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.

Recommended Citation

For any use or distribution of these materials, please cite as follows:

Julie Greenberg, William (Sandy) 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].

Technical Requirements

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

  1. Biomedical Signals and Images
    • ECG: Cardiac electrophysiology, relation of electrocardiogram (ECG) components to cardiac events, clinical applications. Guest lecture.
    • Speech Signals: The source-filter model of speech production, spectrographic analysis of speech.
    • Speech Coding: Analysis-synthesis systems, channel vocoders, linear prediction of speech, linear prediction vocoders.
    • Imaging Modalities: Survey of major modalities for medical imaging: ultrasound, X-ray, 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.
  2. 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 discrete-time systems, convolution.
    • DTFT: The discrete-time Fourier transform and its properties. FIR filter design using windows.
    • DFT: The discrete Fourier transform and its properties, the fast Fourier transform (FFT), the overlap-save algorithm, digital filtering of continuous-time signals.
    • Sampling Revisited: Sampling and aliasing in time and frequency, spectral analysis.
    • Image processing I: Extension of filtering and Fourier methods to 2-D signals and systems.
    • Image processing II: Interpolation, noise reduction methods, edge detection, homomorphic filtering.
  3. 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.
  4. Image Segmentation and Registration
    • Image Segmentation: statistical classification, morphological operators, connected components.
    • Image Registration I: Rigid and non-rigid 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)

  1. ECG Filtering and Frequency Analysis of the Electrogram
    Design filters to remove noise from electrocardiogram (ECG) signals and then design a system to detect life-threatening ventricular arrhythmias. The detector is tested on normal and abnormal ECG signals. (3 weeks - Greenberg)
  2. Speech Coding
    Implement, test, and compare two speech analysis-synthesis systems. These systems utilize a pitch detector and a speech synthesizer based on the source-filter model of speech production. (3 weeks - Greenberg)
  3. Image Segmentation
    Process clinical MRI scans of the human brain to reduce noise, label tissue types, extract brain contours, and visualize 3-D anatomical structures. (2 weeks - Fisher)
  4. Image Registration
    Explore the co-registration of medical images, focusing on 2-D to 2-D (slice to slice) registration and using non-linear optimization methods to maximize various measures of image alignment. (2 weeks - Fisher)
  5. ECG: Blind Source Separation
    Separate fetal and maternal ECG signals using techniques based on second- and higher-order 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. Discrete-Time Signal Processing. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 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: Prentice-Hall, 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: Prentice-Hall, 1978. ISBN: 9780132136037.

Quatieri, T. F. Discrete-Time Speech Signal Processing: Principles and Practice. Upper Saddle River, NJ: Prentice-Hall, 2001. ISBN: 9780132429429.

Image Processing and Medical Imaging

Lim, J. S. Two-Dimensional 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: Prentice-Hall, 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 0-4 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 less-than-thorough 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

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: end-of-term wrap-up Lab 5 due

 




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