Computational Methods and Tools for Decision Support in Biomedicine: An Overview of Algorithmic Challenges

Computational Methods and Tools for Decision Support in Biomedicine: An Overview of Algorithmic Challenges

Ioannis Dimou (Technical University of Crete, Greece), Michalis Zervakis (Technical University of Crete, Greece), David Lowe (University of Aston, UK) and Manolis Tsiknakis (Foundation of Research and Technology Hellas, Greece)
DOI: 10.4018/978-1-60566-314-2.ch001
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Abstract

The automation of diagnostic tools and the increasing availability of extensive medical datasets in the last decade have triggered the development of new analytical methodologies in the context of biomedical informatics. The aim is always to explore a problem’s feature space, extract useful information and support clinicians in their time, volume, and accuracy demanding decision making tasks. From simple summarizing statistics to state-of-the-art pattern analysis algorithms, the underlying principles that drive most medical problems show trends that can be identified and taken into account to improve the usefulness of computerized medicine to the field-clinicians and ultimately to the patient. This chapter presents a thorough review of this field and highlights the achievements and shortcomings of each family of methods. The authors’ effort has been focused on methodological issues as to generalize useful conclusions based on the large number of notable, yet case-specific developments presented in the field.
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Introduction

Contemporary and future methods of healthcare delivery will be exploiting new technology, novel sensing devices and a plethora of modes of information generated by distributed data sources. This raw data is inevitably increasing in volume and complexity at a rate faster than the ability of primary healthcare providers to access and understand it. Several countries are currently considering issues of integrated personalised healthcare and the application of ‘intelligent’ data mining methodologies in providing medical decision support to the clinician (and the individual), using principled pattern recognition methodologies.

Within such an environment, the domain of medical imaging, with its various structural (CT, MRI, U/S) and functional (PET, fMRI) modalities, is probably on the top of the list with respect to the amount of raw data generated. Most of these modalities are explored in other chapters of this volume. Even though image inspection by human experts enables the accurate localization of anatomic structures and/or temporal events, their systematic evaluation requires the algorithmic extraction of certain characteristic features that encode the anatomic or functional properties under scrutiny. Such imaging features, treated as markers of a disease, can subsequently be integrated with other clinical, biological and genomic markers, thus enabling more effective diagnostic, prognostic and therapeutic actions. It is the purpose of this chapter to address issues related to the decision making process, to trace developments in infrastructure and techniques, as well as to explore new frontiers in this area.

The Medical Informatics Revolution

During the last decades we are witnessing a gradual shift in the medical field. Medical professionals are increasingly being supported by advanced sensing equipment. These instruments provide objective information and assist in reducing the margin of error in diagnosis and prognosis of diseases. Detailed imaging techniques provide accurate anatomic and/or functional maps of the human body, and advanced signal processing methods performing biosignal and biochemical analyses are now largely automated, faster and increasingly accurate. In the broader medical research field, larger datasets of patients including multiple covariates are becoming available for analysis.

Figure 1 outlines the information flow in a medical decision support system. At an initial stage, a large amount of data is collected from various sensors and pre-processed. This data is accessibly stored in a structured format and fused with other information, such as expert knowledge. At a higher level, patterns are sought in the full dataset and translated in an intelligent way to produce meaningful and helpful reasoning. This output supports healthcare professionals during their prognostic, diagnostic and other decision making tasks. At the end of this process, feedback to the system in the form of expert evaluation or validity of analysis can be incorporated to improve performance.

Figure 1.

Medical informatics decision support system dataflow

This relative data abundance has resulted in a corresponding explosion of scientific papers referring to thorough statistical analysis with data mining and pattern classification techniques. New findings are more easily made available to the scientific community through the internet and cheap processing power aids the development of complex models of diseases, drugs, and effects.

In this context the field of medical informatics emerges as the intersection of information technology with the different disciplines of medicine and health care. It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, analysis and use of information in health and biomedicine (VanBemmel & Musen, 1997). Medical informatics tools include not only computers but also clinical guidelines, formal medical terminologies, and information, communication and decision support systems. It is by now evident that medical informatics do not just provide information but also summarize it in an intelligent and comprehensive form.

Key Terms in this Chapter

Principal Components Analysis (PCA): Transformation often used to reduce multidimensional data sets to lower dimensions for analysis (also known as Karhunen-Loève or Hotelling transform).

Medical Informatics: Study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information.

Receiver Operating Characteristic: Curve that connects all points defined of corresponding sensitivity-specificity values for varying threshold levels. It is used to visualize a trained classifier’s overall performance irrespective of specific decision thresholds.

Functional magnetic resonance imaging (fMRI): Method to measure the haemodynamic response related to neural activity in the brain or spinal cord.

Grid Computing: A service scheme that facilitates the utilization of the processing and storage resources of many computers as a common infrastructure for specific application domains (scientific etc.).

Information Fusion: The utilization of all available information at multiple abstraction levels (measurements, features, decisions) to maximize an expert system’s performance.

Prognostic/Diagnostic Models: Mathematical/ algorithmic models designed to provide early detection of disease.

Single Photon Emission Computed Tomography (SPECT) Imaging: A nuclear medicine tomographic imaging technique using gamma rays.

Electroencephalogram (EEG): Measurement of postsynaptic electrical activity produced by the brain’s neurons.

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Table of Contents
Preface
Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis
Chapter 1
Ioannis Dimou, Michalis Zervakis, David Lowe, Manolis Tsiknakis
The automation of diagnostic tools and the increasing availability of extensive medical datasets in the last decade have triggered the development... Sample PDF
Computational Methods and Tools for Decision Support in Biomedicine: An Overview of Algorithmic Challenges
$37.50
Chapter 2
William Hsu, Alex A.T. Bui, Ricky K. Taira, Hooshang Kangarloo
Though an unparalleled amount and diversity of imaging and clinical data are now collected as part of routine care, this information is not... Sample PDF
Integrating Imaging and Clinical Data for Decision Support
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Chapter 3
Spyretta Golemati, John Stoitsis, Konstantina S. Nikita
The estimation of motion of the myocardial and arterial wall is important for the quantification of tissue elasticity and contractility and has... Sample PDF
Analysis and Quantification of Motion within the Cardiovascular System: Implications for the Mechanical Strain of Cardiovascular Structures
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Chapter 4
Christos V. Bourantas, Katerina Naka, Dimitrios Fotiadis, Lampros Michalis
Intracoronary Ultrasound (ICUS) imaging is an intravascular catheter-based technique which provides real-time, high resolution, cross-sectional... Sample PDF
New Developments in Intracoronary Ultrasound Processing
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Chapter 5
Stavroula Mougiakakou, Ioannis Valavanis, Alexandra Nikita, Konstantina S. Nikita
Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that... Sample PDF
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
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Chapter 6
Marotesa Voultsidou, J. Michael Herrmann
Indicative features of an fMRI data set can be evaluated by methods provided by theory of random matrices (RMT). RMT considers ensembles of matrices... Sample PDF
Significance Estimation in fMRI from Random Matrices
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Chapter 7
Dimitrios C. Karampinos, Robert Dawe, Konstantinos Arfanakis, John G. Georgiadis
Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water... Sample PDF
Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI
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Chapter 8
Dimitrios G. Tsalikakis, Petros S. Karvelis, Dimitrios I. Fotiadis
Segmentation plays a crucial role in cardiac magnetic resonance imaging (CMRI) applications, since it permits automated detection of regions of... Sample PDF
Segmentation of Cardiac Magnetic Resonance Images
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Chapter 9
Katia Marina Passera, Luca Tommaso Mainardi
Image registration is the process of determining the correspondence of features between images collected at different times or using different... Sample PDF
Image Registration Algorithms for Applications in Oncology
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Chapter 10
Lena Costaridou, Spyros Skiadopoulos, Anna Karahaliou, Nikolaos Arikidis, George Panayiotakis
Breast cancer is the most common cancer in women worldwide. Mammography is currently the most effective modality in detecting breast cancer... Sample PDF
Computer-Aided Diagnosis in Breast Imaging: Trends and Challenges
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Chapter 11
E. Kyriacou, C.I. Christodoulou, C. Loizou, M.S. Pattichis, C.S. Pattichis, S. Kakkos
Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to... Sample PDF
Assessment of Stroke by Analysing Cartoid Plaque Morphology
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Chapter 12
Marios Neofytou, Constantinos Pattichis, Vasilios Tanos, Marios Pattichis, Eftyvoulos Kyriacou
The objective of this chapter is to propose a quantitative hysteroscopy imaging analysis system in gynaecological cancer and to provide the current... Sample PDF
Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer
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Chapter 13
Thomas V. Kilindris, Kiki Theodorou
Patient anatomy, biochemical response, as well functional evaluation at organ level, are key fields that produce a significant amount of multi modal... Sample PDF
Combining Geometry and Image in Biomedical Systems: The RT TPS Case
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Chapter 14
Ioannis Tsougos, George Loudos, Panagiotis Georgoulias, Konstantina S. Nikita, Kiki Theodorou
Quantitative three-dimensional nuclear medical imaging plays a continuously increasing role in radionuclide dosimetry, allowing the development of... Sample PDF
Internal Radionuclide Dosimetry using Quantitative 3-D Nuclear Medical Imaging
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Chapter 15
Evanthia E. Tripoliti, Dimitrios I. Fotiadis, Konstantia Veliou
Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain... Sample PDF
Diffusion Tensor Imaging and Fiber Tractography
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Chapter 16
Anastasios Koutlas, Dimitrios I. Fotiadis
The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression... Sample PDF
Image Processing and Machine Learning Techniques for Facial Expression Recognition
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Chapter 17
Arcangelo Merla
This chapter presents an overview on recent developments in the field of clinical applications of the functional infrared imaging. The functional... Sample PDF
Developments and Advances in Biomedical Functional Infrared Imaging
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Chapter 18
Aristotelis Chatziioannou, Panagiotis Moulos
The completion of the Human Genome Project and the emergence of high-throughput technologies at the dawn of the new millennium, are rapidly changing... Sample PDF
DNA Microarrays: Analysis and Interpretation
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Chapter 19
Nikolaos Giannakeas, Dimitrios I. Fotiadis
Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical... Sample PDF
Image Processing and Machine Learning Techniques for the Segmentation of cDNA
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Chapter 20
Petros S. Karvelis, Dimitrios I. Fotiadis
Automated chromosome analysis is now becoming routine in most human cytogenetics laboratories. It involves both processing and analysis of digital... Sample PDF
Recent Advances in Automated Chromosome Image Analysis
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Chapter 21
O. Lezoray, G. Lebrun, C. Meurie, C. Charrier, A. Elmotataz, M. Lecluse
The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis.... Sample PDF
Machine Learning in Morphological Segmentation
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Chapter 22
Michael Haefner, Alfred Gangl, Michael Liedlgruber, A. Uhl, Andreas Vecsei, Friedrich Wrba
Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images... Sample PDF
Pit Pattern Classification Using Multichannel Features and Multiclassification
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Chapter 23
C. Papaodysseus, P. Rousopoulos, D. Arabadjis, M. Panagopoulos, P. Loumou
In this chapter the state of the art is presented in the domain of automatic identification and classification of bodies on the basis of their... Sample PDF
Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images
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Chapter 24
Alexia Giannoula, Richard S.C. Cobbold
“Elastography” or “elasticity imaging” can be defined as the science and methodology of estimating the mechanical properties of a medium (including... Sample PDF
Nonlinear Ultrasound Radiation-Force Elastography
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Chapter 25
Valentina Russo, Roberto Setola
The aim of this chapter is to provide an overview about models and methodologies used for the Dynamic Contrast Enhancement (DCE) analysis. DCE is a... Sample PDF
Dynamic Contrast Enhancement: Analysis's Models and Methodologies
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Chapter 26
George K. Matsopoulos
The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image... Sample PDF
Automatic Correspondence Methods towards Point-Based Medical Image Registration: An Evaluation Study
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Chapter 27
Alberto Taboada-Crispi, Hichem Sahli, Denis Hernandez-Pacheco, Alexander Falcon-Ruiz
Various approaches have been taken to detect anomalies, with certain particularities in the medical image scenario, linked to other terms... Sample PDF
Anomaly Detection in Medical Image Analysis
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Chapter 28
C. Delgorge-Rosenberger, C. Rosenberger
The authors present in this chapter an overview on evaluation of medical image compression. The different methodologies used in the literature are... Sample PDF
Evaluation of Medical Image Compression
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Chapter 29
Charalampos Doukas, Ilias Maglogiannis
Medical images are often characterized by high complexity and consist of high resolution image files, introducing thus several issues regarding... Sample PDF
Advanced ROI Coding Techniques for Medical Imaging
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Chapter 30
Farhang Sahba
Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images... Sample PDF
Segmentation Methods in Ultrasound Images
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