ANNOUNCEMENT:
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)
Sincerely,
Metin Akay, Ph.D.
Course Organizer
10TH NORDIC-BALTIC CONFERENCE ON BIOMEDICAL ENGINEERING
TUTORIAL COURSE
BIOMEDICAL SIGNAL AND IMAGE PROCESSING:
TIME-FREQUNENCY, WAVELETS, FRACTALS, FUZZY LOGIC, KNOWLEDGE BASED SYSTEMS
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
FUTURE TOPICS AND INVITED SPEAKERS
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
required.
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
scale-space.
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.
