IEEE EMBS'95 Time-Freq, Wavelets, Wavelet Packets in Biomed Engr.

Bill Thompson wgthom at gandalf.rutgers.edu
Wed Apr 19 21:04:44 EST 1995


 ANNOUNCEMENT:
 
 Dear Colleague:
 
 In the upcoming International Conference of the IEEE Engineering in 
 Medicine and Biology Society (EMBS) in Montreal, September 20-23, 1995, a
 1 1/2 days workshop on NEW ADVANCES IN BIOMEDICAL SIGNAL AND IMAGE PROCESSING 
 will be held before the conference 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,
 and Wavelet Packets.
 
 I am pleased to invite you to join us for these exciting presentations
 by prominent experts in engineering, medicine, computer science and
 applied mathematics.
 
 The cost for each attendee will be:
 1. $100 for IEEE student member
 2. $200 IEEE members, 
 3. $300 non IEEE members.
 
 If you are interested in joining us at Montreal, and have any
 questions about the workshop and registrations, please contact me 
 at Rutgers (908-445-4096) or e-mail (akay at gandalf.rutgers.edu).
 
                                                 Sincerely,
                                                 Metin Akay, Ph.D.
                                                 Workshop Chair
 
         17th Annual International Conference of IEEE EMBS
  
                            WORKSHOP
      
          NEW ADVANCES IN BIOMEDICAL SIGNAL AND IMAGE PROCESSING
     
     Time-Frequency, Wavelets, Wavelet Packets in Biomedical Engineering
 
                     Organizing Committee
 
 Tom Brotherton     Patrick Flandrin    Dennis Healy     Andrew Laine
 Mohsine Karrakchou Yves Meyer          Janet Rutledge   Banu Onaral   
 Tim Olson          Malvin Teich        Ahmed Tewfik     Nitish Thakor
 Michael Unser      Victor Wickerhaus   William Williams  Andrew Yagle    
 
 In this workshop, we will focus on the tutorial presentations of advanced 
 signal and image processing methods. These will include new advances in 
 time-frequency and wavelet transform and wavelet packets.
 In addition, biomedical and image processing applications will 
 be presented. The invited speakers are experts in the areas of signal 
 processing, medical imaging, computers science and applied mathematics.
 
 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.

              FUTURE TOPICS AND INVITED SPEAKERS

1. Time-Frequency and Wavelets in Signal and Image Processing:
   Algorithms and Implementations 
  
   Yves Meyer

2. The generalized sampling and the design of discrete/continuous signal 
   processing algorithms
   Michael Unser

3. The survey of the time-frequency analysis method from Wigner to 
   the Modern approaches
   William Williams

4. The recent advances in quadratic time-frequency analysis methods
   Patrick Flandrin

5. Epileptic Seizure Detection Based on Wavelet Analysis of EEG 
   Banu Onaral

6. Wavelet and Point-Process Analysis of Fractal Neural Firing Patterns
   in Audition and Vision
   Malvin Teich

7. Modern Methods in Neurological Signal Processing
   Applications to Detection of Brain Injury
   Nitish Thakor

8. Wavelets and Neural Networks in Maturation 
   Metin Akay

9. The adapted waveform de-noising for medical signals and images
    Victor Wickerhauser

10. The noise reductions and spectral shaping as a combined processing 
    strategy for hearing aids
    Janet Rutledge
11. New Signal Processing Techniques for Physiological Data Analysis
    Mohsine Karrakchou      

12. Multiscale wavelet frames for contrast enhancement of digital radiographs
    Andrew Laine

13. Waveform and beamform design for range and 2-D Doppler ultrasound imaging
    Ahmed Tewfik

14. Reducing the imaging time in MRI
    Dennis Healy

15. Non-linear stabilization of ill-conditioned linear 
   inverse problems via adaptive subspace decomposition
    Tim Olson

16. Inversion of the radon transform under wavelet constraints
    Andrew Yagle

17. The Application of Wavelets and Fuzzy Logic Based Neural Nets to 
    Medical Data and Image Processing Problems
    Tom Brotherton


ABSTRACTS:


1. Time-Frequency and Wavelets in Signal and Image Processing
   Algorithms and Implementations 

   Yves Meyer

Y.M. will provide a panorama on a variety of tools in image and signal 
processing, ranging from time-scale algorithms (i.e. traditional wavelets) 
to time-frequency algorithms like the Wigner distribution and the 
so-called "Malvar-Wilson bases". A new and unexpected result is the fact 
that, in many cases, using a Wigner distribution amounts to decomposing the 
signal (or image) into  orthonormal localized chirps. The location of these 
atoms in the time frequency plane yields a crucial information displayed 
in a geometric picture.

2. Generalized  sampling and the design of discrete/continuous signal
processing algorithms

Michael Unser
Biomedical Engineering and Instrumentation Program,
National Institutes of Health, Bethesda MD 20892-5766

Sampling is the process of representing continuous-time (or space)
functions by  sequences of numbers (discrete signal representation). 
Traditionally, both the signal and its representation are assumed to be
bandlimited.  Here, we lift this hypothesis and present a general procedure
for the approximation of arbitrary finite energy signals from their sampled
measurements at the output a given analog prefilter (e.g., non-ideal
acquisition device).  The approximation spaces that we consider are
generated by translation of a generating kernel $\phi$, a special case
being the conventional sinc interpolator.  This function may also
correspond to the impulse response of a display device, or may be selected
to specify a certain spline or wavelet representation space.  We show that
a consistent signal approximation can be obtained by appropriate digital
filtering of the discrete measurements.  This approximation is essentially
equivalent to the initial signal in the sense that it would result in
exactly the same measurements if it was re-injected into the system.  We
present the conditions under which this scheme yields the optimal least
squares solution and provide general error bounds. The theory is
illustrated with the design of two algorithms.  The first is a spline-based
procedure the minimum error scale-conversion of images (with an arbitrary
scaling factor).  The second is a digital filtering algorithm for the
improvement of image display.


3. Time-Frequency Signal Analysis from Wigner to the Modern Approaches

  W. J. Williams, Professor of EECS University of Michigan

	Time frequency signal analysis will be presented from classical
representations such as the Wigner distribution and the spectrogram. The
concepts of Cohen's class of distributions will be introduced and used
as guides in developing and understanding a variety of new time-frequency
analysis tools which have appeared during the past five years.  The original
concept of design for reduced interference distributions (RIDs) will be
presented. It will be shown how  high resolution in both time and frequency
with sharp reductions in the often troublesome interference terms associated
with the Wigner distribution can be achieved.  A number of modern approaches
which provide improved results and avoid the window trade-offs inherent  in
spectrograms  will be discussed. The desirable properties of time-frequency
distributions such as proper time and frequency marginals, proper time and
frequency support among  others will be discussed in terms of their
realizability  and the tradeoffs inherent in gaining these properties.
Adaptive time-frequency distributions will be discussed in terms of their
benefits and computational complexity.  Discrete practical time frequency
distributions such as the Binomial distribution will be discussed and 
guidelines for practical realizations of real-time applications will be
presented. Time-frequency filtering and signal inversion from time-frequency
distributions will be discussed briefly.  Finally, similarities and
differences between Cohen's class of distributions and wavelet transforms will
be highlighted.


4. Some recent advances in quadratic time-frequency/scale signal analysis and
processing.

                  P. 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.

*results to be presented will summarize joint works with F. Auger, R.G.
Baraniuk and O. Michel.


5. Epileptic Seizure Detection Based on Wavelet Analysis of EEG 

Samir Mehta, Richard W. Koser, Banu Onaral

Scaling Signals and Systems Laboratory
Biomedical Engineering and Science Institute & Electrical and Computer
Engineering Department, Drexel University
College of Engineering
32nd and Chestnut Streets
Philadelphia, Pennsylvania 19104
Phone:215-895-2247
Fax:  215-895-1695
Banu_Onaral at cbis.ECE.Drexel.EDU

The inverse power-law attenuation of the normal EEG spectrum suggests that EEG
exhibits self-similar fluctuations over a multiplicity of scales. Such behavior
is best characterized by measures which capture the scale-invariant nature of
the signal. We investigate the use of discrete wavelet transform as a
multi-scale decomposition tool to monitor the statistical scale-invariant
properties of the EEG in long-term monitoring aimed to localize epileptic foci.
The objective is to detect the onset of seizure marked by loss of
scale-invariance over clinically relevant scales. We develop a scheme which
monitors changes in the scaling properties of EEG independent of waveform
morphology. Results obtained by processing clinical data are presented.


6. WAVELET AND POINT-PROCESS ANALYSIS OF FRACTAL NEURAL FIRING PATTERNS IN
AUDITION AND VISION

Malvin C. Teich
Columbia University
New York, New York

The existence of long-duration temporal correlation (i.e., memory) in
the spike trains of peripheral auditory neurons is well established.
Such correlation, extending to time scales at least five orders of
magnitude greater than the refractory period, has been shown to be
present in all primary afferent auditory neurons of the cat,
chinchilla, and chicken. The upper limit of the observed correlation
time is imposed by the duration of the recording. More proximally,
correlation has also been found in the firing patterns of lateral
superior olivary auditory neurons inthe cat. Recently, we have examined
the response variability and correlation properties of spontaneous and
stimulated spike trains at three loci in the cat visual system:
retinal-ganglion cells, lateral-geniculate-nucleus cells in the
thalamus, and striate-cortex neurons. We have also investigated the
response of an insect visual interneuron, the descending contralateral
movement detector of the locust. The Fano-factor time curves, and
wavelet analysis, reveal that long-duration temporal correlation is
also present in the firing patterns of these visual-system neurons. The
spike trains generated by all of the visual-system neurons, as well as
those generated by primary auditory neurons, can be discribed by a
refractoriness-modified fractal stochastic point process.

Malvin C. Teich, Professor
Department of Electrical Engineering
Columbia University
500 West 120 Street
New York, New York 10027, USA
Tel: (212) 854-3117
FAX: (212) 932-9421
EMAIL: mct2 at columbia.edu

7. Modern Methods in Neurological Signal Processing: 
   Applications to Detection of Brain Injury


Nitish V. Thakor, Xuan Kong, David Sherman
Biomedical Engineering Department
Johns Hopkins School of Medicine
Baltimore, MD 21205, USA
Tel: 410-955-7093; Fax: 410-955-0549
email: nthakor at bme.jhu.edu

	Brain injury resulting from events such as hypoxia or ischemia 
have important clinical correlates such as asphyxic injury in neotates 
and stroke in adults.  Similarly, in high risk neurological 
surgeries or neurological critical care unit there is a need to rapidly 
detect possible incidence of brain injury.  This  presentation 
will review the origins and responses of neurological signals such as 
electroencephalogram and evoked potentials during 
experimental studies of brain  injury.   Four different methods 
of analysis of neurological signals will be presented.  
Results obtained using five methods (adaptive Fourier series 
modeling, adaptive coherence analysis, wavelet analysis, 
time-frequency analysis, and bispectral analysis) will be presented.   
Theoretical innovations and new experimental results 
will be discussed.

8. WAVELETS AND NNs in MATURATION


Metin Akay
Dept. of Biomedical Engineering, Rutgers University, Piscataway,
NJ 08855

Spontaneous breathing movements in the fetus tend to occur
intermittently and do not become continuous until after birth. In both
the primate and the ovine species, breathing movements have been
observed with a high degree of variability in instantaneous breathing
rates. Previously, the analysis of fetal breath
signals is limited to statistical description of the time intervals,
or a measure of short-term and long-term fluctuations using the fast
Fourier transform.

In this study, use of the matching pursuit and NNs revealed that
fluctuations in instantaneous fetal breathing rates were much more
complicated than previously thought. The most interesting finding is
the presence of sinusoidal-like activities which were indicated by the
long horizontal structures in the energy distribution. 
Although fast Fourier transform had indicated substantial slow modulation in
breathing rates, it was not apparent that there were sinusoidal-like
activities.

9.	Adapted Waveform De-Noising for Medical Signals and Images

	    Ronald R. Coifman		Mladen Victor Wickerhauser
	     Yale University		 Washington University

We describe some new libraries of waveforms well-adapted to various numerical
analysis and signal processing tasks.  The main point is that by expanding a
signal in a library of waveforms which are well-localized in both time and
frequency, one can achieve both understanding of structure and efficiency in
computation.  We briefly cover the properties of the new ``wavelet packet''
and ``localized trigonometric'' libraries.  The main focus will be
applications of such libraries to the analysis of complicated transient
signals: a feature extraction and data compression algorithm for speech
signals which uses best-adapted time and frequency decompositions, and an
adapted waveform analysis algorithm for removing fish noises from hydrophone
recordings.  These signals share many of the same properties as EEG traces,
but with distinct features that are easier to characterize and detect.

10. Noise Reduction and Spectral Shaping as a Combined Processing
    Strategy for Hearing Aids

Janet C. Rutledge
Assistant Professor
EECS Department		
Northwestern University
2145 Sheridan Road
Evanston, IL  60208-3118

Persons suffering from sensorineural hearing loss experience an
elevated threshold of hearing and reduced dynamic range of hearing.
Therefore hearing aids must incorporate amplification plus some sort
of amplitude limiting.  In addition, frequency and temporal resolution
are normally reduced which causes an increased sensitivity to
background noise.  Hearing aids, which generally amplify all incoming
sounds, exacerbate this problem.

Techniques employing multi-channel amplitude compression have been
proposed to compensate for the elevated threshold and reduced dynamic
range of hearing.  We show that wavelet parameterization methods can
be used for time-varying, frequency-dependent amplitude compression
which adapts to the incoming signal on a frame-by-frame basis.  In
addition, we propose a wavelet-based noise reduction system which
distinguishes between speech and noise using a single-microphone input
that can be used as a front end to this amplitude compression system.



11. New Signal Processing Techniques for Physiological Data Analysis
Mohsine Karrakchou      
Ecole Polytechnique Federale de Lausanne       Tel:(4121) 693 26 05
Signal Processing Lab                          Fax:(4121) 693 76 00
Swiss Federal Institute of Technology          e-mail: mohsine at ltssg3.epfl.ch
CH-1015 Lausanne Switzerland                   www: http://ltswww.epfl.ch/

The aim of this paper is to present and validate algorithms allowing
the bedside estimation of the effective pulmonary capillary pressure.
The method should be automatic, i.e. should require no user
intervention beyond inflating the balloon. It should be usable even if
the pulmonary arterial pressure signal is distorted by respiratory
pressure variations, (provided that the distortions are not massive).
In this way, the estimation of $P_{mv}$ will no longer require that
the patient be paralyzed and apneic, thus it will be more generally
applicable. To achieve these requirements, three points are
intensively investigated, namely, the cancellation of respiratory
interference, the determination of the occlusion point and finally the
estimation of the model parameters. New algorithms have been developed
for each of these different topics.

For the cancellation of respiratory interference, the approach
consists in using an adaptive interference canceller scheme in which
the auxiliary signal employed is the right atrial pressure signal. The
latter can be recorded simultaneously with pulmonary artery pressure
using the same catheter. New structures for subband adaptive filtering
based on wavelet packets have been developed for this purpose

For the determination of the starting point or more generally for
singularity detection, the continuous wavelet transform as well as the
orthogonal wavelet transform have been investigated. An original
nonlinear scheme for multiscale analysis has been proposed to achieve
the same objective improving the performance. The latter is inspired
from continuous wavelet transform and is based on morphological
operators.

Concerning the parameter estimation, the underlying model of a double
exponential and the peculiar nature of the present artifacts made the
classical least square fitting method inappropriate. Therefore, an
entirely new method for estimating the parameters of a linear
combination of decaying exponentials has been developed. The core of
the method is the Hough transform usually used in image processing.

The developed algorithms were tested and validated on AO data obtained
in an experimental model of lung injury and in intensive care patients
presenting pulmonary hypertension. Excellent results have been
obtained for the estimation of $P_{mv}$. Beside the important step
forward that has been made for the $P_{mv}$ estimation, the newly
developed algorithms are not only useful for the present application
but also for many others. 




-- 
12. Multiscale Wavelet Frames for Contrast Enhancement of Digital Radiographs

  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

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.

13. Waveform and Beamform Design for range and 2-D Doppler 
    Ultrasound Imaging

Y. Kadah and A. H. Tewfik
Dept. Of Electrical Engineering, Univ. Of Minnesota

We consider the problem of producing an "optimal" range- 2-D 
Doppler image in a finite amount of time. A range- 2-D Doppler 
provides estimates of  the transverse and line-of-sight flows as a 
function of range. By optimal image, we mean the closest image in a 
2-norm sense to the actual the transverse and line-of-sight flows 
distribution as a function of range. We show that to obtain that 
optimal image we need to adjust both the beamform and waveform 
of the transmitted ultrasound field to the flow distribution. We 
propose a beamform and waveform selection strategy that leads to a 
progressive refinement of resolution of the produced image along 
the range, line-of-sight and transverse flow axes. In this approach, 
the operator can at each step select the parameter for which 
maximum improvement will occur. We discuss imaging scenarios in 
which the returns due to the optimal beamforms and waveforms can 
be "synthesized" from those corresponding to a  standard beamform 
and waveform and scenarios where one actually needs to generate 
different beamforms and waveforms to improve the quality of the 
final image. 


We have developed a solution to thsi problem that allows simultaneous 
estimation of both components by properly selecting a sequence of 
waveforms (which happen to be wavelets in the frequency domain). 

14.     Reducing the Imaging Time in Magnetic Resonance Imaging

                      Dennis Healy
                   Dartmouth College

I will describe two approaches to the problem of reducing
imaging time in Magnetic Resonance Imaging (MRI).
First we consider the advantages and disadvantages of using a 
Karhunen-Loeve (K-L) expansion of a training set of images to reduce the 
number of encodes required for a Magnetic Resonance (MR) image of a new object.
We evaluate the error likely to be achieved as a function of the number of
encodes and two technical problems: reduced SNR in the images and 
smoothing of theK-L functions in practice.  We propose the use of localized
trigonometric bases developed by Coifman et. al. as an alternative to the
K-L basis.  The localized trigonometric bases approach the error 
achieved by the K-L basis, but they are easier to use with existing methods
for fast acquisition.

Next I'll summarize some recent work concerning the imaging of time-varying
objects from a sequence of projections taken during the evolution
of the object. Reconstruction of coarse features, corresponding to low 
spatial-frequency data, can be made nearly instantaneously in time from the 
evolving data. A temporal sequence of these low spatial-frequency
reconstructions can be used to estimate the motion of the object.
Once the motion is estimated, we may use the estimate to compensate for
some of the motion of fine scale features. 
This enables accurate reconstructions of the time varying fine structure in 
several cases. The algorithm is demonstrated for a joint motion case study.
In general, this technique shows promise for a wide variety of applications 
in MRI, and fast imaging using x-ray CT.

Clinical applications should include both functional MRI such as
dynamic imaging of oxygen usage and blood flow in the brain, and
motion imaging of joints, angiography in the lungs, and heart imaging.


 
15. Non-linear stabilization of ill-conditioned linear 
inverse problems via adaptive subspace decomposition"

             Tim Olson
            Dartmouth College

We will investigate methods to stabilize ill-conditioned 
linear inverse problems.  These methods will utilize 
non-linear constraints, and subspace decompositions which 
preserve the non-linear constraints to stabilize ill-conditioned 
linear problems.  One application of these methods is to 
limited angle tomography.


16. Inversion of the Radon Transform under Wavelet Constraints

 Berkman Sahiner and Andrew E. Yagle
 The University of Michigan, Ann Arbor

We investigate two applications of the wavelet transform to the problem of 
image reconstruction from projections.  The first application is nonlinear and
spatially-varying filtering of reconstructed noisy images.  We constrain 
wavelet coefficients of the reconstructed noisy image to be zero in 
certain regions of the time-scale plane, and compute the minimum 
mean-square estimate of the image given the statistics of the additive 
noise and the constraints.  

We also discuss how the constraints may be obtained from the noise-corrupted 
image.  The second application is image restoration in a limited-angle 
reconstruction problem.  We propose a wavelet-based restoration algorithm 
given some approximate a priori knowledge about the edges that lie 
parallel to the missing view angles.  We use this approximate partial edge 
knowledge to restore certain affected high-resolution wavelet domain images.  
The low-resolution image is restored using interpolation, and the 
wavelet transform is used to combine the low-resolution 
and high-resolution images into a complete reconstructed image.  
Numerical examples illustrate the improvement achieved in both applications.


17. The Application of Wavelets and Fuzzy Logic Based Neural Nets to 
     Medical Data and Image Processing Problems


                 Tom Brotherton
                 Pat Simpson
                 ORINCON Corporation

Fuzzy logic and neural network techniques have been gaining popularity in the
processing of medical data.  Presented here is the application of general
approach which uses a hierarchy of neural nets coupled with advanced feature
processing, such as wavelet transformations, to solve medical signal and image
processing problems.  These include the determination of the degree of
stenosis in the carotid artery using a single channel of Doppler ultrasound
where both FFT and wavelet transforms are used to characterize the data for
input to the nets; the determination of the severity of coronary artery
disease in stress test ECG wave forms; and the determination of cardiac
tissues and structures in echocardiogram images.  The presentation will focus
primarily on neural nets but will also discuss the relationship of frequency
transformations and wavelet processing to form inputs to neural nets for the
development of signal and image classification systems.  The neural networks
that we use, called Fuzzy Min-Max (FMM) neural networks are based on fuzzy
sets.  The use of fuzzy logic based neural nets is particularly relevant for
medical applications as they have nearest neighbor properties that allow the
user to determine "why" a network gave the results it did and thus have
explanatory capabilities that allow the user to determine where test patterns
did or did not overlap with training patterns.  













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