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Tutorial Biomed. Sig. Proc., Finland'96

Bill Thompson wgthom at gandalf.rutgers.edu
Wed Nov 8 01:36:11 EST 1995

 Dear Colleague:
 In the upcoming International Conference of the 10th Nordic-Baltic
 Conference in Tampere, Finland, June 9-13, 1996, a 2 day tutorial
 course  on  BIOMEDICAL SIGNAL AND IMAGE PROCESSING  will be held before the
 conference (June 8-9, 1995) to discuss and present new advances in 
 biomedical signal and image processing methods and recent applications 
 of these emerging technologies including Time-Frequency, Wavelet Transform,
 Fractals, Fuzzy Logic and Knowledge Based Systems.
 I am pleased to invite you to join us for these exciting presentations
 by prominent experts in engineering, medicine, computer science and
 applied mathematics.

I would also appreciate it if you could bring this announcement to the
attention of your colleagues by posting the attached invitation
to newsgroups and bulletin boards at your departments or institutions.

 The cost for each attendee will be:

         Before  April 7, 1996           After April 7, 1996

Regular       1000 FIM                    1400 FIM
Student        650 FIM                    1400 FIM 

 If you are interested in joining us at Tampere, and have any
 questions about the tutorial and registrations, please contact me 
 at Rutgers (908-445-4906) or e-mail (akay at gandalf.rutgers.edu) or
 Dr. Rami Lehtinen at Ragnar Granit Institute (358-31-316-2524) or 
 e-mail (rami at ee.tut.fi). The conference program can be found from 
 the WWW-page of the  conference (http://www.ee.tut.fi/~nbc96)

                                                 Metin Akay, Ph.D.
                                                 Course Organizer


                       TUTORIAL  COURSE



This symposium is intent for both tutorial and the biomedical
applications of biomedical signal and image processing method
including time-frequency, wavelets, fractals, fuzzy logic  and
knowledge based systems. 

The first part will cover the theory behind the 
time-frequency and wavelet transform methods, definitions and
properties of WTs including the fast algorithms for continuous and
fast discrete  wavelet transforms, and the 2-D implementation of
wavelet transform with medical image applications. 
In addition, the applications of the time-frequency and wavelet 
transforms to the the respiratory, EEG, auditory, evoked potential
response signals, and medical image analyses using wavelet transforms 
will be included.  

The second part will include the hybrid signal processing methods
the combination of the fractals, maximum likelihood and waveletswith
biomedical applications.

The third part will cover the fuzzy logic and knowledge based systems
and their applications to medicine

We encourage engineers, medical researchers, computers scientists and applied
mathematicians to learn about recent developments in signal and image 
processing and their applications in biomedical engineering.

                                            Metin Akay                   
                                            Organizer and Chair


1. Design and Implementation of Time-Frequency and Wavelets Methods
    Patrick Flandrin
    ENS Lyon, France

Two families of results will be presented, which are believed to give new
motivations for using quadratic (Wigner-type) time-frequency/scale
representations in signal analysis and processing, and which are both based
on an interpretation in terms of 'distributions' or 'densities'.
First, a mechanical analogy will be used for constructing sharply localized
representations which circumvent the usual trade-off between joint
resolution and cross-terms level. The approach relies on the concept of
'reassignment', associated to displacement operators on the plane.
Second, a probabilistic analogy will be put forward for attaching
information and dissimilarity measures to representations, thus allowing to
compare signals from their time-frequency content.

This talk will review some useful analysis methods, such as the
Short-Time Fourier Transform, the Gabor Representation, the Wigner-Ville
Distribution, the Exponential Distribution, and the Wavelet
Transforms including orthogonal, biorthogonal and nonorthogonal
wavelet transform methods. The basic concepts behind these methods 
as well as their limitations, implementation, and applications 
are presented.

2. Design and Implementation of 2-D Wavelet Transforms

  Andrew Laine
  Computer and Information Sciences Department
  301 Computer Science and Engineering Builiding
  P.0. Box 116120
  University of Florida
  Gainesville, FL  32611

  Email: laine at cis.ufl.edu
  Phone/Fax: (904) 392-1239

In addition to the analysis of 1-D biomedical signals, the wavelet
transforms offer numerous advantages over the traditional Fourier
transform for the analysis of medical imaging including the MRI, CAT,
ultrasound because of the simultaneous time and frequency localization
characteristics of the wavelet transforms.
gives an overview of the 1-D and 2-D discrete
wavelet transform, the data compression of the digital mammography and
teleradiology. He also discusses the applications of wavelet transform
methods for feature enhancement and classification.

3.              Hybrid Signal Processing Methods: 
         Wavelets, Maximum Likelihood and Fractals 
                         Metin Akay
                Biomedical Engineering Department
        Rutgers University, P.O. Box 909, Piscataway, NJ 08855
       Phone/Fax: (908) 445-4906, e-mail: akay at caip.rutgers.edu

Fractional Brownian motion (FBM) provides a useful model for
many physical phenomena demonstrating long-term dependencies and
1/f-type spectral behavior.  In this model, only one parameter
is necessary to describe the complexity of the data, H the Hurst
exponent.  FBM is a nonstationary random function not well
suited to traditional power spectral analysis however.  In this
talk we discuss alternative methods for the analysis of FBM, in
the context of real-time biomedical signal processing. 
Regression-based methods utilizing the power spectral density
(PSD), the discrete wavelet transform (DWT), and dispersive
analysis (DA) are compared for estimation accuracy and precision
on synthesized FBM datasets.  The performance of a maximum
likelihood estimator (MLE) for H, theoretically the best possible
estimator, are presented for reference.  Of the regression-based
methods, it is found that the estimates provided by the combination 
of the DWT and MLE Methods have better accuracy and 
precision for estimating the fractal dimension of signals. 
The PSD method was biased in a nonlinear manner.
In addition, the applications of wavelet based fractal estimators
in medicine will be presented.

4. Wavelets in Biomedical Engineering

                         Metin Akay
                Biomedical Engineering Department
        Rutgers University, P.O. Box 909, Piscataway, NJ 08855
       Phone/Fax: (908) 445-4906, e-mail: akay at caip.rutgers.edu

Here, we would like to discuss some potential applications of
the wavelet transform to biological signals.
i. The Analysis of Phrenic Neurogram:

The objective of the analysis was to characterize eupnea (normal
breathing) and to understand how system perturbations such as hypoxia
result in alterations in respiratory patterning in
both the time and frequency domains.

ii. The Analysis of Diastolic Heart Sounds:

The objective of the analysis was to investigate the use of
wavelet analysis to analyze the turbulent sounds associated with coronary
artery disease and to provide a simple, noninvasive approach for the
detection of coronary  artery disease.

Results suggested that the detail signals from the normal subject have
no activity in the first three wavelet bands. 
However, the detail signals in the fourth and especially the fifth
wavelet bands are prominent, suggesting that the diastolic 
heart sounds from normal subjects do not have any significant
high frequency components.

iii.  The Analysis of Evoked Response Activity:

The objective of the analysis was to characterize the short latency
evoked potentials that can be observed in human subjects following
stimulation of respiratory mechanoreceptors. These respiratory-related
evoked responses indicate the nature of afferent information entering
the central regulatory and perception processes. We have recently
explored the wavelet transform method for improved signal detection
in noisy backgrounds, and applied the wavelet transform method to our
data to determine whether we could obtain the essential
characteristics of the signal in fewer trials that was previously

Results suggested that the respiratory evoked response signal used in
this study has low frequency signal components throughout time, but
intermediate frequency signal components only between 50-100ms with
several transients in time. The wavelet transform was able to localize
the transients and  the intermediate frequency components between
50-100 msec. 

5. Wavelets and Medical Imaging

   Andrew Laine
  Computer and Information Sciences Department
  301 Computer Science and Engineering Builiding
  P.0. Box 116120
  University of Florida
  Gainesville, FL  32611

This talk shall describe a novel approach for accomplishing mammographic
feature analysis by overcomplete multiresolution representations.
We show that efficient  representations may be identified  within a
continuum of scale-space and used to enhance  features of importance to
mammography. We present methods of contrast enhancement based on three
multiscale representations: (1) The dyadic wavelet transform
(separable), (2) The phi-transform (non-separable, non-orthogonal),
and (3) The hexagonal wavelet transform (non-separable).

Multiscale features identified within distinct levels of transform space
provide local support for image enhancement. Mammograms are reconstructed
>From wavelet coefficients modified at one or more levels by local and global
non-linear operators. In each case, multiscale edges and gain parameters
are selected adaptively by a measure of energy within each level of 
We show quantitatively that transform coefficients, modified within each
level by adaptive non-linear operators, can make more obvious unseen or
barely seen features of mammography without requiring additional radiation.
Our results are compared with traditional image enhancement techniques by
measuring the local contrast of known mammographic features.

6.       Fuzzy Logic and Knowledge-Based Systems in Medicine

                 Klaus-Peter Adlassnig

Univ.Prof. DI Dr. techn. Klaus-Peter Adlassnig     Tel.:  +43-1-40866993
Department of Medical Computer Sciences            Fax:    +43-1-4052988
University of Vienna Medical School
Waehringer Guertel 18-20                           E-mail:
A - 1090 Vienna, Austria                           kpa at akh-wien.ac.at

Fuzzy set theory and fuzzy logic have a number of
characteristics that make them highly suitable to model uncertain
information upon which medical concept forming, state interpretation,
and diagnostic as well as therapeutic decision making is usually based.
Firstly, inexact medical entities including entities with temporal
properties can be definied as fuzzy sets.  Secondly, fuzzy logic offers
reasoning methods capable of drawing approximate and uncertain
inferences.  Finally, fuzzy automata may be used as high level
monitoring devices.

These facts suggests that fuzzy set theory might be a applicable basis
for developing knowledge-based systems for interpretation, diagnosis,
treatment, and monitoring tasks.  This is verified by trials performed
with the following systems:

1. Cadiag, a medical expert consultation system for internal 
medicine based on fuzzy sets and fuzzy inference,  
2.  Onset, an interpretative system supporting the
diagnosis of toxoplasmosis that applies prototypical fuzzy courses 
representing possible antibody variations, 
3. Diamon, an intelligent on-line monitoring
program for  ICU (intensive care unit) data from patients with
adult respiratory distress syndrome and employing fuzzy trend detection
and fuzzy automata. 

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