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Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing

Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing

Manuela Pereira (University of Beira Interior, Portugal) and Mario Freire (University of Beira Interior, Portugal)
Copyright: © 2011 |Pages: 396
ISBN13: 9781605662800|ISBN10: 1605662801|EISBN13: 9781605662817
DOI: 10.4018/978-1-60566-280-0
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MLA

Pereira, Manuela, and Mario Freire. "Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing." IGI Global, 2011. 1-396. Web. 27 Mar. 2020. doi:10.4018/978-1-60566-280-0

APA

Pereira, M., & Freire, M. (2011). Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing (pp. 1-396). Hershey, PA: IGI Global. doi:10.4018/978-1-60566-280-0

Chicago

Pereira, Manuela, and Mario Freire. "Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing." 1-396 (2011), accessed March 27, 2020. doi:10.4018/978-1-60566-280-0

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The massive volume of data that some medical and biological applications generate require special processing resources that guarantee privacy and security, creating a crucial need for cluster and grid computing.

Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing disseminates knowledge regarding high performance computing for medical applications and bioinformatics. Containing a defining body of research on the subject, this critical reference source includes a valuable collection of cutting-edge research chapters for those working in the broad field of medical informatics and bioinformatics.

Table of Contents

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Front Materials
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Title Page
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Copyright Page
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Preface
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Chapters
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Chapter 1
Przemyslaw Lenkiewicz (University of Beira Interior & Microsoft Portugal, Portugal), Manuela Pereira (University of Beira Interior, Portugal), Mário M. Freire (University of Beira Interior, Portugal), José Fernandes (Microsoft Portugal, Portugal)
This chapter contains a survey of the most popular techniques for medical image segmentation that have been gaining attention of the researchers and medical practitioners since the early 1980s until present time. Those methods are...
Techniques for Medical Image Segmentation: Review of the Most Popular Approaches
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Chapter 2
S. Jehan-Besson (LIMOS CNRS, France), J. Fadili (GREYC CNRS, France), G. Née (GREYC CNRS, France), G. Aubert (GREYC, France & General Electric Healthcare, France)
In this chapter, we focus on statistical region-based active contour models where the region descriptor is chosen as the probability density function of an image feature (e.g. intensity) inside the region. Image features are then...
Medical Image Segmentation and Tracking Through the Maximisation or the Minimisation of Divergence Between PDFs
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Chapter 3
T. Heida (University of Twente, The Netherlands), R. Moroney (University of Twente, The Netherlands), E. Marani (University of Twente, The Netherlands)
Deep Brain Stimulation (DBS) is effective in the Parkinsonian state, while it seems to produce rather non-selective stimulation over an unknown volume of tissue. Despite a huge amount of anatomical and physiological data regarding...
Modeling and Simulation of Deep Brain Stimulation in Parkinson’s Disease
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Chapter 4
Olivier Bockenbach (RayConStruct GmbH, Germany), Michael Knaup (Institute of Medical Physics, Germany), Sven Steckmann (Institute of Medical Physics, Germany), Marc Kachelrieß (Institute of Medical Physics, Germany)
Commonly used in medical imaging for diagnostic purposes, in luggage scanning, as well as in industrial non-destructive testing applications, Computed Tomography (CT) is an imaging technique that provides cross sections of an object...
High-Performance Image Reconstruction (HPIR) in Three Dimensions
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Chapter 5
Frédéric Payan (Université de Nice - Sophia Antipolis, France), Marc Antonini (Université de Nice - Sophia Antipolis, France)
The modelling of three-dimensional (3D) objects with triangular meshes represents a major interest for medical imagery. Indeed, visualization and handling of 3D representations of biological objects (like organs for instance) are...
Compression of Surface Meshes
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Chapter 6
Filipe Soares (University of Beira Interior & Siemens S.A. Healthcare Sector, Portugal), Mário M. Freire (University of Beira Interior, Portugal), Manuela Pereira (University of Beira Interior, Portugal), Filipe Janela (Siemens S.A. Healthcare Sector, Portugal), João Seabra (Siemens S.A. Healthcare Sector, Portugal)
The improvement on Computer Aided Detection (CAD) systems has reached the point where it is offered extremely valuable information to the clinician, for the detection and classification of abnormalities at the earliest possible...
The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis
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Chapter 7
Constantino Carlos Reyes-Aldasoro (The University of Sheffield, UK), Abhir Bhalerao (University of Warwick, UK)
In recent years, the development of new and powerful image acquisition techniques has lead to a shift from purely qualitative observation of biomedical images towards more a quantitative examination of the data, which linked with...
Volumetric Texture Analysis in Biomedical Imaging
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Chapter 8
Ana Leiria (Universidade do Algarve, Portugal), M. M. M. Moura (Universidade do Algarve, Portugal)
A broad view on the analysis of Doppler embolic signals is presented, uniting physics, engineering and computing, and clinical aspects. The overview of the field discusses the physiological significance of emboli and Doppler...
Analysis of Doppler Embolic Signals
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Chapter 9
Pedro Tomás (INESC-ID / IST TU Lisbon, Portugal), IST TU Lisbon (INESC-ID / IST TU Lisbon, Portugal), Aleksandar Ilic (INESC-ID / IST TU Lisbon, Portugal), Leonel Sousa (INESC-ID / IST TU Lisbon, Portugal)
When analyzing the neuronal code, neuroscientists usually perform extra-cellular recordings of neuronal responses (spikes). Since the size of the microelectrodes used to perform these recordings is much larger than the size of the...
Massive Data Classification of Neural Responses
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Chapter 10
Thomas V. Kilindris (University of Thessaly, Greece), Kiki Theodorou (University of Thessaly, Greece)
Patient anatomy, biochemical response, as well functional evaluation at organ level, are key fields that produce a significant amount of multi modal information during medical diagnosis. Visualization, processing, and storage of...
Combining Geometry and Image in Biomedical Systems: The RT TPS Case
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Chapter 11
Xiu Ying Wang (BMIT Research Group at The University of Sydney, Australia), Dagan Feng (University of Sydney, Australia & Hong Kong Polytechnic University, China)
The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new challenges in medical knowledge discovery from multi-imaging modalities and management. In...
Image Registration for Biomedical Information Integration
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Back Materials
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Compilation of References
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About the Contributors
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Index
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