Integrating Imaging and Clinical Data for Decision Support

Integrating Imaging and Clinical Data for Decision Support

William Hsu (University of California at Los Angeles, USA), Alex A.T. Bui (University of California at Los Angeles, USA), Ricky K. Taira (University of California at Los Angeles, USA) and Hooshang Kangarloo (University of California at Los Angeles, USA)
DOI: 10.4018/978-1-60566-314-2.ch002
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Abstract

Though an unparalleled amount and diversity of imaging and clinical data are now collected as part of routine care, this information is not sufficiently integrated and organized in a way that effectively supports a clinician’s ability to diagnose and treat a patient. The goal of this chapter is to present a framework for organizing, representing, and manipulating patient data to assist in medical decision-making. We first demonstrate how probabilistic graphical models (specifically, Bayesian belief networks) are capable of representing medical knowledge. We then propose a data model that facilitates temporal and investigative organization by structuring and modeling clinical observations at the patient level. Using information aggregated into the data model, we describe the creation of multi-scale, temporal disease models to represent a disease across a population. Finally, we describe visual tools for interacting with these disease models to facilitate the querying and understanding of results. The chapter concludes with a discussion about open problems and future directions.
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Introduction

More patient data is being gathered, given the adoption of the electronic medical record (EMR), the availability of clinical tests, and the growing rates of chronic diseases (Wyatt & Wright, 1998). Modern medical records are not only comprised of traditional data (e.g., clinical notes, labs), but also digital images (e.g., computed tomography, magnetic resonance imaging) and other graphical representations (e.g., pulmonary function graphs). Notably, medical imaging is becoming the predominant in vivo tool for objectively documenting patient presentation and clinical findings. Patient care is largely dependent upon imaging to understand disease processes and to establish tangible evidence of treatment response. However, even within imaging, the scope of data collected can range from the cellular level (e.g., molecular imaging) to tissue level (e.g., histopathology), up to the level of the organism itself (e.g., conventional radiology). As the quantity and diversity of collected data continues to grow, the task of consolidating this information in a way that improves patient care becomes a challenge: clinicians need effective tools to organize, access, and review the data. For instance, current methods for querying image data are limited to a set of keywords (e.g., stored as part of the image header), but much of the clinically useful information about a disease is contained within the image itself (e.g., mass volume, border, shape). Advances in image processing have resulted in sophisticated algorithms for automated content extraction (Pham, Xu, & Prince, 2000), enabling the characterization, segmentation, and classification of pixel data to extract meaningful features from an image (e.g., regions of edema). However, the extraction of meaningful features from patient data alone is insufficient. While imaging data provides a phenotypic description of disease progression, the combination of imaging and other clinical observations has the potential to better model and predict disease behavior. A collective understanding of how features from different levels are needed: a finding observed at the phenotype level can be explained as an emergent manifestation of multiple findings at the genotype level. For instance, the cellular level serves as the basis for describing genetic/proteomic irregularities that lead to larger scale effects that are seen at the tissue, organ, and organism levels. While research in the area of intelligent data analysis (IDA) has explored content extraction and representation, current approaches have significant limitations: 1) they do not capture the context in which the data was collected; 2) the data is not represented in a way that facilitates a computer’s ability to reason with the information; and 3) a lack of tools exists for facilitating the querying and understanding of the stored data.

This chapter describes efforts, particularly those undertaken by the Medical Imaging Informatics Group at the University of California, Los Angeles (UCLA), to address these issues by transforming clinical observations into a representation enabling a computer to “understand” and reason with the data. Computer understanding, in this context, is defined as being able to determine the relative importance of a given data element (e.g., necrosis size) in the patient record in relation to a phenomenon of interest (e.g., brain tumor). The chapter is organized as follows: Section 2 provides an overview of IDA and recent work in the area towards creating expert systems. While various techniques for representing medical knowledge exist, this chapter focuses on probabilistic graphical models. Section 3 introduces a phenomenon-centric data model (PCDM) that structures clinical observations at the patient level by organizing findings (i.e., collected data) around a given phenomenon (i.e., medical problem). Section 4 describes the process of generating multi-scale, temporal disease models using dynamic Bayesian belief networks to represent a disease across a population: these steps are illustrated in the context of our efforts to develop tools that help assess and manage patients with brain tumors. Subsequently, Section 5 discusses a novel interface for querying these models using a visual paradigm to facilitate the composition of queries related to image features. The chapter concludes by describing open problems in the area and identifying potential directions for future work.

Key Terms in this Chapter

Intelligent Data Analysis: The use of statistical, pattern recognition, machine learning, data abstraction, and visualization tools for analysis of data and discovery of mechanisms that created the data.

Probabilistic Graphical Model: A graph that represents independencies among random variables by a graph in which each node is a random variable and missing edges represent conditional independencies.

Data Mining: The principle of sorting through large amounts of data and picking out relevant information.

Dynamic Bayesian Network: A directed graphical model of stochastic processes that generalize hidden Markov models and are typically used to model a time series.

Visual Query Interface: A tool that enables user to visually interact with the underlying graphical model and guides the user through the query formulation process by adapting the interface based on the structure of the model.

Graphical Metaphor: Unique and identifiable visual representations of variables specified in the disease model.

Bayesian Belief Network: A directed acyclic graph that represents a set of variables and their probabilistic independencies.

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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
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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
$37.50
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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