# Signal and image processing of physiological data: methods for diagnosis and treatment purposes

Anne Humeau-Heurtier, Edite Figueiras, Joao Cardoso

► To cite this version:

Anne Humeau-Heurtier, Edite Figueiras, Joao Cardoso. Signal and image processing of physiological data: methods for diagnosis and treatment purposes. Computational and Mathematical Methods in Medicine, 2016, 2016 (1054605), pp.2. 10.1155/2016/1054605. hal-01392027

HAL Id: hal-01392027

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Submitted on 23 Jan 2024

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# Signal and image processing of physiological data: methods for diagnosis and treatment purposes

Anne Humeau-Heurtier, Edite Figueiras, Joao Cardoso

<figure><img src="image_2.png" /></figure>

## ► To cite this version:

Anne Humeau-Heurtier, Edite Figueiras, Joao Cardoso. Signal and image processing of physiological data: methods for diagnosis and treatment purposes. Computational and Mathematical Methods in Medicine, 2016, 2016 (1054605), pp.2. 10.1155/2016/1054605. hal-01392027

HAL Id: hal-01392027

https://hal.science/hal-01392027v1

Submitted on 23 Jan 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Distributed under a Creative Commons Attribution 4.0 International License
Editorial

# Signal and Image Processing of Physiological Data: Methods for Diagnosis and Treatment Purposes

Anne Humeau-Heurtier,<fnref n="1" /> Edite Figueiras,<fnref n="2" /> and Joao Cardoso<fnref n="3" />

Correspondence should be addressed to Anne Humeau-Heurtier; anne.humeau@univ-angers.fr

Received 29 February 2016; Accepted 1 March 2016

Copyright © 2016 Anne Humeau-Heurtier et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Signals and images have taken a major place in clinical
practice to assist medical experts in the decision-making
process. They are now commonly used for research activities
or in clinical routines, for the diagnosis or treatment of
pathologies. However, this decision-making process requires
the development of signal and image processing algorithms.
This is why, in the clinical domain, signal and image process-
ing algorithms aiming at extracting information, quantifying
processes, or improving the visualization of medical data have
exploded. New algorithms overcoming the existing ones by
their better computational cost or by providing additional or
more accurate information are still proposed. Moreover, new
applications for the new and existing algorithms emerge. This
is true for any kind of medical data but the physiological and
pathophysiological questions offer a variety of applications
and situations. This includes the study and understanding of
adaptive responses in health and pathophysiological mecha-
nisms in disease, at any level of physiological organization,
ranging from molecules to humans. Moreover, adaptive,
integrative, and translational physiology is concerned.

This special issue proposes different papers in this way of
using computers to help in the diagnosis and in the therapy.
Thus, for the field of medical computer vision, H. Li et al.
propose a 3D facial feature point localization method that
combines the relative angle histograms with multiscale con-
straints. Moreover, to help the medical staff in combining
images coming from different modalities into one image,

P. Geng et al. introduce a medical image fusion algorithm
in spatial domain that uses an adaptive manifold filter. In
the denoising field, L. Chang et al. propose a two-stage MRI
denoising algorithm that is based on 3D optimized blockwise
version of nonlocal means and multidimensional PCA. An
insight into the relationship between the transient auditory-
evoked potentials and synthetic steady-state responses at
different stimulus rates under the superposition hypothesis
is proposed by X. Tan et al., whereas M. Alvarado-González
et al. introduce a study to describe a P300, an event-
related potential endogenous component. A. Shakeel et al.
have written a review of the features used for EEG data
recording from different studies that used movement-related
cortical potential to predict the upcoming real or imaginary
movement. R. Faltermeier et al. propose an extension of a
mathematical tool set, called selected correlation analysis,
which detects positive and negative correlations between
arterial blood pressure and intracranial pressure. This exten-
sion optimizes the parameters of the selected correlation
analysis. U. Mäder et al. introduce an analysis procedure
of digital microscopic images to increase diagnostic quality
and to shorten the time-to-diagnosis for superficial fungal
infections. F. Espinoza-Cuadros et al. investigate the use of
both patients' facial images and voice recordings processing
to estimate the apnea-hypopnea index that describes the
severity of the obstructive sleep apnea. S. Tagliaferri et al.
use acceleration and deceleration phase rectified slope of

<footnote n="1">¹Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), University of Angers, 62 avenue Notre-Dame du Lac, 49000 Angers, France</footnote>

<footnote n="2">²Ultrafast Bio- and Nanophotonics Group, International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga, 4715-330 Braga, Portugal</footnote>

<footnote n="3">³Department of Physics, LIBPhys-UC, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal</footnote>
Editorial

# Signal and Image Processing of Physiological Data: Methods for Diagnosis and Treatment Purposes

Anne Humeau-Heurtier,<fnref n="1" /> Edite Figueiras,<fnref n="2" /> and Joao Cardoso<fnref n="3" />

Correspondence should be addressed to Anne Humeau-Heurtier; anne.humeau@univ-angers.fr

Received 29 February 2016; Accepted 1 March 2016

Copyright © 2016 Anne Humeau-Heurtier et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Signals and images have taken a major place in clinical
practice to assist medical experts in the decision-making
process. They are now commonly used for research activities
or in clinical routines, for the diagnosis or treatment of
pathologies. However, this decision-making process requires
the development of signal and image processing algorithms.
This is why, in the clinical domain, signal and image process-
ing algorithms aiming at extracting information, quantifying
processes, or improving the visualization of medical data have
exploded. New algorithms overcoming the existing ones by
their better computational cost or by providing additional or
more accurate information are still proposed. Moreover, new
applications for the new and existing algorithms emerge. This
is true for any kind of medical data but the physiological and
pathophysiological questions offer a variety of applications
and situations. This includes the study and understanding of
adaptive responses in health and pathophysiological mecha-
nisms in disease, at any level of physiological organization,
ranging from molecules to humans. Moreover, adaptive,
integrative, and translational physiology is concerned.

This special issue proposes different papers in this way of
using computers to help in the diagnosis and in the therapy.
Thus, for the field of medical computer vision, H. Li et al.
propose a 3D facial feature point localization method that
combines the relative angle histograms with multiscale con-
straints. Moreover, to help the medical staff in combining
images coming from different modalities into one image,

P. Geng et al. introduce a medical image fusion algorithm
in spatial domain that uses an adaptive manifold filter. In
the denoising field, L. Chang et al. propose a two-stage MRI
denoising algorithm that is based on 3D optimized blockwise
version of nonlocal means and multidimensional PCA. An
insight into the relationship between the transient auditory-
evoked potentials and synthetic steady-state responses at
different stimulus rates under the superposition hypothesis
is proposed by X. Tan et al., whereas M. Alvarado-González
et al. introduce a study to describe a P300, an event-
related potential endogenous component. A. Shakeel et al.
have written a review of the features used for EEG data
recording from different studies that used movement-related
cortical potential to predict the upcoming real or imaginary
movement. R. Faltermeier et al. propose an extension of a
mathematical tool set, called selected correlation analysis,
which detects positive and negative correlations between
arterial blood pressure and intracranial pressure. This exten-
sion optimizes the parameters of the selected correlation
analysis. U. Mäder et al. introduce an analysis procedure
of digital microscopic images to increase diagnostic quality
and to shorten the time-to-diagnosis for superficial fungal
infections. F. Espinoza-Cuadros et al. investigate the use of
both patients' facial images and voice recordings processing
to estimate the apnea-hypopnea index that describes the
severity of the obstructive sleep apnea. S. Tagliaferri et al.
use acceleration and deceleration phase rectified slope of

<footnote n="1">¹Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), University of Angers, 62 avenue Notre-Dame du Lac, 49000 Angers, France</footnote>

<footnote n="2">²Ultrafast Bio- and Nanophotonics Group, International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga, 4715-330 Braga, Portugal</footnote>

<footnote n="3">³Department of Physics, LIBPhys-UC, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal</footnote>
computerized cardiotocographic traces for the identification and management of intrauterine growth restriction of fetuses and in the prediction of the neonatal outcome. In the field of laryngeal functionality, using endoscopic images, S. Peter-mann et al. investigated analytical approaches for computing the voice onset time. Finally, M. Zhu et al. introduce an improved niche genetic algorithm for feature reduction in high-dimensional data.

The wide biomedical engineering community (both med-
ical and scientists communities) is expected to be interested
by this special issue. Both researchers and practitioners will
find results to go ahead in the diagnosis and treatment of
pathologies.

**Disclosure**

Edite Figueiras formerly worked at Tampere University of
Technology and BioMeditech, Finland.

Anne Humeau-Heurtier
Edite Figueiras
Joao Cardoso
computerized cardiotocographic traces for the identification and management of intrauterine growth restriction of fetuses and in the prediction of the neonatal outcome. In the field of laryngeal functionality, using endoscopic images, S. Petermann et al. investigated analytical approaches for computing the voice onset time. Finally, M. Zhu et al. introduce an improved niche genetic algorithm for feature reduction in high-dimensional data.

The wide biomedical engineering community (both medical and scientists communities) is expected to be interested by this special issue. Both researchers and practitioners will find results to go ahead in the diagnosis and treatment of pathologies.

## Disclosure

Edite Figueiras formerly worked at Tampere University of Technology and BioMeditech, Finland.

Anne Humeau-Heurtier
Edite Figueiras
Joao Cardoso
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Evidence-Based
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Hindawi

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<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

CAM

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<figure><img src="image_21.png" /></figure>

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Evidence-Based
Complementary and
Alternative Medicine

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Oxidative Medicine and
Cellular Longevity