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Computerized Medical Imaging and Graphics 34 (2010) 415–417
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Computerized Medical Imaging and Graphics
journal homepage: www.elsevier.com/locate/compmedimag
Guest editorial
Introduction to the special issue on biomedical image technologies and methods
1. Introduction
Today, medical technology news more and more often reports
on recent developments in the field of biomedical image technologies and systems able to detect the most subtle changes in the
human body and prepared to provide early diagnoses and tools for
efficient treatment management and planning. It is indeed a fact
that medical and molecular imaging today drives advancements
in healthcare technologies, leading the way towards the concept
of the personalized medicine that is gaining impulse nowadays.
Non-invasive (or at least minimally invasive) imaging procedures
allow physicians to evaluate a patient’s condition within minutes
and take decisions on the proper therapy even in emergency cases,
such like myocardial infarctions, etc. It is true that virtually all significant medical acts today involve imaging procedures, either for
diagnosis, treatment planning or therapy follow-up: imaging technologies now form an inseparable element of modern medicine,
which would hardly advance without their assistance.
The aim of this special issue is to provide a snapshot of emerging technologies and methods in biomedical imaging, based on
a selected number of papers presented in the 8th IEEE International Conference on Bio-Informatics and Bio-Engineering (BIBE
2008). The best scoring contributions to the Conference during the
peer evaluation process in the tracks of Bioimaging and Biomedical Image Processing have been invited to submit extended and
updated papers for publication in this special issue. A total of 21
works were initially invited and 19 papers have been submitted,
although one of them has been somehow deviated and published
in an earlier issue of this journal. Five papers have been considered as inappropriate for publication in this special issue after the
peer review process, during which at least two reviewers (and up to
four in some cases) have anonymously evaluated each submission.
Of the 13 papers finally included in this special issue, 7 works have
required two review rounds before their acceptance for publication.
2. Paper summaries
A novel method for unsupervised DNA microarray gridding
based on Support Vector Machines (SVMs) is presented in the
work by Bariamis et al. [1], in their attempt to provide a robust
separation method of the spots visible in DNA microarrays into
distinct cells. The proposed method has been evaluated on reference microarray images containing more than two million spots
in total and the researchers have proven its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image
rotation.
0895-6111/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compmedimag.2010.06.001
Paul et al. [2] propose a new and simple method to quantify
the signal dependent and non-signal dependent components of
the noise from a single fluorescence microscopy image, without
the need to use a reference image. Moreover, their method is computationally efficient and permits the on-line quantification of the
noise in the acquired images.
The paper by Gorpas et al. [3] proposes a novel method to
“see” a tumor embedded in tissue, which is a turbid medium, in
fluorescence molecular imaging. This problem has not been fully
encountered yet, due to the non-linear nature of the inverse problem. Through the proposed methodology, the results of a forward
solver based on a region of interest are given when comparing the
simulated and the acquired data. With this modus operandi a-priori
information about the initial fluorophore distribution becomes
available, leading to a more feasible inverse problem solution.
A system for integrated analysis of fluorescence micrographs of
tissue samples is presented by Herold et al. [4]. The system combines image segmentation and information visualization principles
and is proved to achieve high accuracy evaluation of the images and
considerably speeds up the process.
The problem of nuclear segmentation in cancer tissue imaging
that is critical for specific protein activity quantification and for cancer diagnosis and therapy is investigated by Cataldo et al. [5]. A fully
automated morphology-based technique able to perform accurate
nuclear segmentations in images with heterogeneous staining and
multiple tissue layers is presented. The proposed method gave better results when it was compared with a semi-automated method
based on active contours on the segmentation of immunohistochemical images.
The paper by Al-Diri et al. [6] presents an algorithm that forms
a retinal vessel graph. The network connectivity is based on cost
functions modeled through self-organizing feature maps (SOFMs),
while overlapping vessels are handled with specialized algorithms.
The algorithm is tested on junctions drawn from the public-domain
DRIVE database.
Iakovidis et al. [7] propose a novel methodology for screening of
the small intestine with Wireless Capsule Endoscopy (WCE), which
is a revolutionary, relatively non-invasive imaging technique performed by a wireless swallowable endoscopic capsule transmitting
thousands of video frames per examination. In this work a data
reduction algorithm is applied according to a novel scheme for the
extraction of representative video frames from a full length WCE
video. The technique has been experimentally validated, demonstrating that a reduction up to 85% in the reading times can be
achieved for the clinical examiners.
An integrated and interactive decision support system for
the automated melanoma recognition of the dermoscopic images
416
Guest editorial / Computerized Medical Imaging and Graphics 34 (2010) 415–417
based on image retrieval by content and multiple expert fusion is
presented in Ref. [8]. The goal of this work is to support decisionmaking by retrieving and displaying relevant past cases as well
as predicting the image categories (e.g., melanoma, benign and
dysplastic nevi) by combining outputs from different classifiers.
A thresholding-based segmentation method is employed and the
lesion-specific local color and texture features are extracted for
classification. The method is shown to be effective and might constitute the basis of a clinical application operating in real time.
Size-adapted segmentation of individual microcalcifications in
mammography based on microcalcification scale-space signature
estimation of multiscale active contours is presented in the paper
by Arikidis et al. [9]. The proposed method achieved an area overlap
of 0.61 ± 0.15 (when compared to manual delineations), statistically outperforming two other segmentation methods it was
compared to. Moreover, the proposed method performed equally
well for both small (<460 ␮m) and large (≥460 ␮m) microcalcifications.
Noise in medical images reduces the effectiveness of texture analysis and the accuracy of medical diagnosis. In Ref. [10]
seven texture measurement methods are applied to computed
tomography (CT) lung tumor images under noisy conditions. The
susceptibility of each texture measure to noise is investigated,
aiming to enable physicians to use an automated lung texture
classification system in order to choose the appropriate feature
extraction algorithm. Low dose levels are used in dedicated breast
CT, resulting in considerable noise levels, compared to conventional
mammography.
A new fuzzy distributed algorithm for assessing white matter connectivity in the brain using diffusion-weighted magnetic
resonance images is presented in Ref. [11]. Fuzzy connectedness
tractography can use any number of fiber orientations per voxel
and can therefore be combined with any model-based or modelfree reconstruction technique that provides such estimates. In this
work, Sotiropoulos et al. illustrate the performance of the method
using diffusion tensor and Q-ball imaging and assess its flexibility in
the way neighboring voxel similarity is assessed, thus allowing the
incorporation of different affinity functions and image modalities
that may even be application-specific.
A new method for motion estimation of tagged cardiac magnetic
resonance sequences based on variational techniques is presented
by Carranza et al. [12]. The variational method has been improved
by adding a new term in the optical flow equation that incorporates
tracking points with high stability of phase. Results were obtained
through simulated and real data, and were validated by manual
tracking and with respect to a reference state-of-the-art method:
harmonic phase imaging (HARP). The error, measured in pixels
per frame, obtained with the proposed variational method is one
order of magnitude smaller than the one achieved by the reference
method, and it requires a lower computational cost.
Wang et al. [13] present a novel automated monocular video
monitoring approach to recover the human pose in conditions with
persistently heavy obscuration, in their effort to study sleep apnoea.
They employ video monitoring to analyze the covered human body
during sleep in order to analyze postural changes and activities, like
periodic limb movements during sleep that might have diagnostic
value for sleep apnoea and other conditions.
3. Concluding remarks
Biomedical imaging will undoubtedly constitute one indispensable element towards the current vision in healthcare for
personalized, preventive, and cost-effective medicine. In order
to materialize this vision, the joint coordinated development of
research areas at multilevel scale, from molecular and cellular
biological imaging up to the clinical and epidemiological level in
medical imaging is considered of utmost importance. To serve this
goal, a coordinated and homogeneous program for the deployment of imaging infrastructures is necessary, as well as the wide
provision of access and continuous training to state-of-the-art
medical and molecular imaging instrumentation and technologies,
and their sustained development via basic research, multidisciplinary projects and related activities.
Bold initiatives towards the above mentioned goals have already
been initiated worldwide. The European Institute for Biomedical
Imaging Research (EIBIR, www.eibir.org) has been recently established in order to support networking activities in research, and to
spread good practice and promote common initiatives and interoperability in the field of biomedical imaging research. The aim is
to generate critical mass and help coordinate research into new
instrumentation, new methods, concepts and technologies that
would eventually develop the field further and lead to improved
diagnosis, and treatment, as well as prevention of disease. On the
other hand, a joint initiative of EIBIR with the European Molecular
Biology Laboratory (EMBL) has created a roadmap of the European Strategy Forum for Research Infrastructures (ESFRI) towards
the establishment of the European Research Infrastructure for
Imaging Technologies in Biological and Biomedical Sciences (EuroBioImaging, www.eurobioimaging.eu).
The papers in this special issue have covered a wide range of theoretical developments, methodological approaches and concrete
innovative applications, demonstrating the promising potential of
biomedical imaging technologies and methods for the field of modern healthcare.
Acknowledgements
The guest editors wish to thank all the participants in the IEEE
BIBE 2008, which led to this special issue, and especially, the
authors of this special issue for contributing such high quality
papers. We would also like to thank the referees who have critically
evaluated the papers.
References
[1] Bariamis D, Maroulis D, Iakovidis DK. Unsupervised SVM-based gridding for
DNA microarray images. Comput Med Imag Graph 2010;34:418–25.
[2] Paul P, Duessmann H, Bernas T, Huber H, Kalamatianos D. Automatic noise
quantification for confocal fluorescence microscopy images. Comput Med Imag
Graph 2010;34:426–34.
[3] Gorpas D, Yova D, Politopoulos K. A new method for processing the forward solver data in fluorescence molecular imaging. Comput Med Imag Graph
2010;34:435–45.
[4] Herold J, Zhou L, Abouna SS, Pelengaris S, Epstein D, Khan M, et al. Integrating
automated semantic annotation and information visualization for the analysis
of multichannel fluorescence micrographs from pancreatic tissue. Comput Med
Imag Graph 2010;34:446–52.
[5] Cataldo SD, Ficarra E, Acquaviva A, Macii E. Achieving the way for automated
segmentation of nuclei in cancer tissue images through morphologybased approach: a quantitative evaluation. Comput Med Imag Graph
2010;34:453–61.
[6] Al-Diri BI, Hunter A, Steel D, Habib M. Automated analysis of retinal vascular
network connectivity. Comput Med Imag Graph 2010;34:462–70.
[7] Iakovidis D, Tsevas S, Polydorou A. Reduction of capsule endoscopy reading times by unsupervised image mining. Comput Med Imag Graph
2010;34:471–8.
[8] Rahman M, Bhattacharya P. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images. Comput Med
Imag Graph 2010;34:479–86.
[9] Arikidis NS, Karahaliou A, Skiadopoulos S, Korfiatis P, Likaki E, Panayiotakis G,
et al. Size-adapted microcalcification segmentation in mammography utilizing
scale-space signatures. Comput Med Imag Graph 2010;34:487–93.
[10] Al-Kadi OS. Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images.
Comput Med Imag Graph 2010;34:494–503.
[11] Sotiropoulos SN, Bai L, Tench CR. Fuzzy anatomical connectedness of the brain
using single and multiple fibre orientations estimated from diffusion MRI. Comput Med Imag Graph 2010;34:504–13.
Guest editorial / Computerized Medical Imaging and Graphics 34 (2010) 415–417
[12] Carranza N, Bajo A, Sroubek F, Santamarta C, Cristobal G, Santos A, et al. Motion
estimation of tagged cardiac magnetic resonance images using variational techniques. Comput Med Imag Graph 2010;34:514–22.
[13] Wang C-W, Hunter A, Gravill N, Matusiewicz S. Real time pose recognition
of covered human for diagnosis of sleep apnoea. Comput Med Imag Graph
2010;34:523–33.
Guest Managing Editor
George Kontaxakis ∗
E.T.S.I. Telecomunicación, Universidad Politécnica de
Madrid, Spain
Guest Editor
Nikolaos Bourbakis
Engineering College, Wright State University,
OH, USA
417
Guest Editor
Konstantina S. Nikita
Department of Electrical and Computer Engineering,
National Technical University of Athens, Greece
Guest Editor
Constantinos S. Pattichis
Department of Computer Science, University of
Cyprus, Cyprus
∗ Corresponding
author. Tel.: +34 91 336
7366/4253; fax: +34 91 337 7323.
E-mail address: [email protected]
(G. Kontaxakis)