Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images

Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images

Stavroula Mougiakakou (National Technical University of Athens, Greece), Ioannis Valavanis (National Technical University of Athens, Greece), Alexandra Nikita (University of Athens and Diagnostic Imaging Center for the Woman and Child, Greece) and Konstantina S. Nikita (National Technical University of Athens, Greece)
DOI: 10.4018/978-1-60566-314-2.ch005
OnDemand PDF Download:


Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that promise to increase the efficiency of physicians during their clinical practice. This chapter provides a brief overview of the use of computational intelligence methods in the design and development of DSSs aimed at the differential diagnosis of hepatic lesions from Computed Tomography (CT) images. Furthermore, examples of DSSs developed by our research team for supporting the diagnosis of focal liver lesions from non-enhanced CT images are presented.
Chapter Preview


Hepatic diseases, including disorders that cause the liver to function improperly or cease its function (e.g. hepatitis and cirrhosis), are one of the most common diseases all over the world. According to the National Center for Health Statistics, the American Liver Foundation, and the United Network for Organ Sharing over 26.000 people in the United States die each year from chronic liver disease and cirrhosis, while according to the Office for National Statistics in the United Kingdom, liver disease is now the fifth most common cause of death after heart disease, stroke, chest disease and cancer. Hepatic cancer is one of the fastest growing cancers in the United States, while the number of Hepatocellular Carcinomas (HCC), type of primary liver cancer and one of the top eight most common cancers in the world, is rising worldwide. HCC is much more common outside the United States, representing 10% to 50% of malignancies in Africa and parts of Asia. Advances in imaging technologies permit the non-invasive detection and diagnosis of liver of both diffuse hepatic disease, like hepatitis and cirrhosis, and focal liver lesions like cysts, hemangiomas and HCC. The diagnosis can be performed through a wide array of medical imaging techniques including Ultrasonography (US), Magnetic Resonance (MR) imaging, and Computed Tomography (CT) with or without contrast agents. The choice of imaging test depends on the clinical question, availability of the test, patient’s condition and clinician’s familiarity with the test. US imaging is inexpensive, widely available, can easily detect cysts, but its diagnostic accuracy depends strongly on the operator and his/her experience. CT and MR imaging are more sensitive in detecting focal liver lesions. MR imaging although accurate in detecting and differentiating liver lesions, is very expensive and therefore not very popular. The most commonly used image-based detection method of liver lesions is CT due to its short acquisition time, wide imaging range, high spatial resolution, and relatively low cost. Although the quality of liver images has lately improved, it is difficult even for an experienced clinician to discriminate various types of hepatic lesions with high accuracy and without the need for diagnosis confirmation by means of contrast agents (related with renal toxicity or allergic reactions).

Rapid development of computer science permitted the design and development of computerized systems able to assist radiologists in the interpretation, early detection and diagnosis of abnormalities from hundreds of medical images every day. These systems are known as Diagnostic Support Systems (DSSs). Recent advances in DSSs demonstrated that the application of digital image processing techniques along with advanced Computational Intelligence (CI) methods increase the efficiency, diagnostic confidence and productivity of radiologists, acting as a “second” opinion to the clinician.

The main areas of computerized analysis of liver images are: i) general image preprocessing in order to improve the quality of hepatic images; ii) registration of images in case of multi data sets; iii) manually, semi- or fully-automatic segmentation of Regions of Interest (ROIs) corresponding to anatomical structures and/or liver lesion; iv) visualization into two- and/or three-dimensional (2D and/or 3D) space of liver lesions for diagnosis, surgery, radiation therapy planning, quantitative studies and final presentation purposes; v) image analysis for the detection of an abnormality and its classification into one out of several types of liver tissues. Generally, a DSS includes tools based on image processing techniques in order to support all the above mentioned techniques of computerized analysis, while the intelligence is provided through the usage of CI based algorithms embedded into the DSS. CI algorithms belong to Artificial Intelligence (AI) methods and are able to handle complex data characterized by non-linearities.

It is worth mentioning that a DSS can be combined with computer based medical image archiving and management systems following certain information protocols, e.g. DICOM and HL7. Furthermore, a DSS can support telematic technologies, in order to permit the remote diagnosis and tele-consultation between health care professionals, and can be integrated with the electronic medical patient record (Mougiakakou, to appear).

This chapter presents an overview of advanced computer analysis methods used to provide intelligent diagnostic support in the assessment of focal liver lesions from CT images. Furthermore, specific DSS architectures, developed by the Biomedical Simulations and Imaging (BIOSIM) Laboratory, aimed at supporting the diagnosis of focal liver lesions from non-enhanced CT images, are presented. Some future perspectives in the area of DSSs and concluding remarks are also given.

Key Terms in this Chapter

Texture Features: Numerical descriptors calculated from the intensity of pixels in a given image region that characterize the texture (roughness) of this region.

Artificial Neural Networks: Information processing systems with interconnected components analogous to neurons that mimic biological nervous systems and the ability to learn through experience.

Computational Intelligence: A branch of computer science that develops algorithms and techniques to imitate some cognitive abilities, like recognition, learning and evolution.

Feature Selection: Strategy for selecting a sub-set of variables from an initial set towards reducing the dimensionality of input vector to a classifier and building more robust learning models.

Ensemble of Classifiers: A set of classifiers whose individual predictions are fused through a combining strategy.

Diagnostic Support Systems: Computer programs that assist a physician or a health professional during the diagnostic process.

Genetic Algorithms: Algorithms which vary a set of parameters and evaluate the quality or “fitness” of the results of a computation as the parameters are changed or “evolved”.

Non-Enhanced CT Images: CT images obtained without administration of contrast agents.

Complete Chapter List

Search this Book:
Editorial Advisory Board
Table of Contents
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
About the Editors
About the Contributors