Foreword ...................................................... xiv
Preface ....................................................... xvi
Acknowledgment ................................................ xix
Section 1 Introduction
The introduction part includes one chapter, written by the
editors, that presents an overview of the main topics covered
by the book, together with promising research directions that
provide insights on how to use machine learning to tackle
with the image understanding problem.
Chapter 1
From Biomedical Image Analysis to Biomedical Image
Understanding Using Machine Learning ............................ 1
Eduardo Romero, National University of Colombia, Colombia
Fabio González, National University of Colombia, Colombia
The chapter presents an overview of the main topics covered
by the book, emphasizing the fundamental concepts and
techniques. The last part of the chapter focuses on the
main problem in image analysis, image understanding (i.e.,
the problem of relating the low-level visual content of an
image with its high-level semantic meaning).
Section 2 Feature Extraction
Section 2 focuses on the feature extraction process, which
is fundamental for any image analysis task. In the context
of biomedical image analysis, feature extraction is
particularly important since it facilitates the inclusion
of problem specific knowledge in the process.
Chapter 2
Computer-Aided Detection and Diagnosis of Breast Cancer
Using Machine Learning, Texture and Shape Features ............. 27
Geraldo Braz Júnior, Federal University of Maranhão,
Brazil
Leonardo de Oliveira Martins, Pontiphical Catholic
University of Rio de Janeiro, Brazil
Aristófanes Corrêa Silva, Federal University of Maranhão,
Brazil
Anselmo Cardoso de Paiva, Federal University of Maranhão,
Brazil
Chapter two focuses on the problem of breast cancer diagnosis
supported by computerized analysis of digital mammograms.
The chapter discusses different techniques, giving especial
attention to methods that use texture and shape features to
characterize tissues.
Chapter 3
Machine Learning for Automated Polyp Detection in Computed
Tomography Colonography ........................................ 54
Abhilash Alexander Miranda, Université Libre de Bruxelles,
Belgium
Olivier Caelen, Université Libre de Bruxelles, Belgium
Gianluca Bontempi, Université Libre de Bruxelles, Belgium
Chapter three proposes two different features for codifying
the shape characteristics of polyps, and non-polyps, in
computed tomography colonography. The features are
orientation independent and their calculation is not
computationally demanding. The features are tested using
different state-of-the-art machine learning algorithms,
showing a good performance on polyp detection.
Section 3 Machine Learning Based Segmentation
Section 3 is devoted to the problem of image segmentation
using machine learning techniques. Image segmentation is
one of the main problems in image analysis. In biomedical
image analysis, segmentation has several applications such
as localization of pathologies, organ extraction for
morphometry analysis, and cell quantification in histology
slides.
Chapter 4
Variational Approach Based Image Pre-Processing Techniques
for Virtual Colonoscopy ........................................ 78
Dongqing Chen, University of Louisville, USA
Aly A. Farag, University of Louisville, USA
Robert L. Falk, Jewish Hospital & St. Mary's Healthcare,
USA
Gerald W. Dryden, University of Louisville, USA
Chapter four addresses the problem of colon segmentation for
computed tomographic colonography using a variational
approach. This approach uses a statistical model for regions
based on Gaussian functions with adaptive parameters, which
are learned using maximum likelihood estimation. Finally,
pixels are classified as tissue or non-tissue using
a Bayesian classifier.
Chapter 5
Machine Learning for Brain Image Segmentation ................. 102
Jonathan Morra, University of California Los Angeles, USA
Zhuowen Tu, University of California Los Angeles, USA
Arthur Toga, University of California Los Angeles, USA
Paul Thompson, University of California Los Angeles, USA
In chapter five the authors cast image segmentation as
a supervised learning problem in a Bayesian framework.
The chapter presents a new algorithm, AdaSVM, a method
that combines AdaBoost, as a feature selection method,
with a support vector machine classifier. The algorithm
shows a competitive performance when compared to other
state-of-the-art approaches for supervised brain image
segmentation.
Chapter 6
A Genetic Algorithm-Based Level Set Curve Evolution for
Prostate Segmentation on Pelvic CT and MRI Images ............. 127
Payel Ghosh, Portland State University, USA
Melanie Mitchell, Portland State University, USA;
Santa Fe Institute, USA
James A. Tanyi, Oregon Health and Science University,
USA; Oregon State University, USA
Arthur Hung, Oregon Health and Science University, USA
In chapter six the authors propose a genetic algorithm for
optimizing the parameters of a segmenting contour implicitly
defined by a level-set. The genetic algorithms attempts to
minimize an energy function associated to the level-set
function. The algorithm is applied to the problem of
prostate segmentation in pelvic CT and MRI images.
Chapter 7
Genetic Adaptation of Level Sets Parameters for Medical
Imaging Segmentation .......................................... 150
Dário A. B. Oliveira, Catholic University of Rio de
Janeiro, Brazil
Raul Q. Feitosa, Catholic University of Rio de Janeiro,
Brazil
Mauro M. Correia, Unigranrio and National Cancer
Institute-INCA, Brazil
Chapter seven proposes an analogous method to the previous
one. The main difference is that in this method the genetic
algorithm is not used to directly adapt the parameters of
the segmenting curve. Instead, the genetic algorithm is used
to estimate the parameters of an algorithm that attempts to
fit a Gaussian curve to the organ's slice histogram in order
to model the level-set propagation speed. The method is
tested with a liver segmentation task on computer tomography
medical images.
Section 4 Biomedical Image Understanding and Interpretation
Section 4 is dedicated to the problem of understanding the
image contents by structuring the biomedical knowledge with
very different strategies. Automated extraction of
biomedical knowledge is a challenging but necessary task in
the current technological world, in which large amounts of
information are available but not utilized.
Chapter 8
Automatic Analysis of Microscopic Images in Hematological
Cytology Applications ......................................... 167
Gloria Díaz, National University of Colombia, Colombia
Antoine Manzanera, ENSTA-ParisTech, France
In chapter eight the authors explore a great variety of
methods to detect, classify and measure objects in
hematological cytology: the most relevant image processing
and machine learning techniques used to develop a fully
automated blood smear analysis system. Likewise, recent
advances of main automated analysis steps are presented.
Chapter 9
Biomedical Microscopic Image Processing by Graphs ............. 197
Vinh-Thong Та, Université de Caen Basse-Normandie,
ENSICAEN, CNRS, France
Olivier Lézoray, Université de Caen Basse-Normandie,
ENSICAEN, CNRS, France
Abderrahim Elmoataz, Universite de Caen Basse-Normandie,
ENSICAEN, CNRS, France
Chapter nine overviews graph-based regularization methods.
These methods have been extended to address semi-supervised
clustering and segmentation of any discrete domain that can
be represented by a graph of arbitrary structure. These
graph-based approaches are combined to attack various
problems in cytological and histological image filtering,
segmentation and classification.
Chapter 10
Assessment of Kidney Function Using Dynamic Contrast
Enhanced MR1 Techniques ....................................... 214
Melih S. Asian, University of Louisville, USA
Hossam Abd El Munim, University of Louisville, USA
Aly A. Farag, University of Louisville, USA
Mohamed Abou El-Ghar, University of Mansoura, Egypt
In chapter ten the kidney is segmented using level sets and
then classified under three different metrics: Euclidean
distance, Mahalanobis distance and least square support
vector machine. Classification accuracy, diagnostic
sensitivity, and diagnostic specificity result to be 84%,
75%, and 96%, respectively.
Chapter 11
Ensemble of Neural Networks for Automated Cell Phenotype
Image Classification .......................................... 234
Loris Nanni, Università di Bologna, Italy
Alessandra Lumini, Università di Bologna, Italy
Chapter eleven is focused on the study of machine learning
techniques for cell phenotype image classification and
demonstrates the advantages of using a multi-classifier
system instead of a stand-alone method to solve this
difficult classification problem.
Chapter 12
Content-Based Access to Medical Image Collections ............. 260
Juan C. Caicedo, National University of Colombia,
Colombia
Jorge E. Camargo, National University of Colombia,
Colombia
Fabio A. González, National University of Colombia,
Colombia
Chapter twelve describes state-of-the art techniques for
accessing large collections of medical images, retrieving
similar images to the examined one or visualizing the
structure of the whole collection. Both strategies take
advantage of image contents, allowing users to find or
identify images that are related by their visual
composition. In addition, these strategies are based on
machine learning methods to handle complex image patterns,
semantic medical concepts, image collection visualizations
and summariza-tions.
Section 5 Complex Motion Analysis
Section 5 is devoted to the problem of motion analysis, which
adds a time, dynamic dimension to image analysis and
understanding. In this context, motion analysis is understood
in two different and complementary senses: first, a user
interacting with an image using an image visualization
interface, second, structures changing through time in a
sequence of images.
Chapter 13
Predicting Complex Patterns of Human Movements Using
Bayesian Online Learning in Medical Imaging Applications ...... 283
Francisco Gómez, National University of Colombia, Colombia
Fabio Martínez, National University of Colombia, Colombia
Eduardo Romero, National University of Colombia, Colombia
In chapter thirteen the authors present a Bayesian framework
which is able to follow different complex user movements.
The Bayesian strategy is implemented through a particle
filter, resulting in real time tracking of these complex
patterns. Two different imaged patterns illustrate the
potential of the procedure: a precise tracking a pathologist
in a virtual microscopy context and a temporal follow up of
gait patterns.
Chapter 14
Left Ventricle Segmentation and Motion Analysis in
MultiSlice Computerized Tomography ............................ 307
Antonio Bravo, Universidad Nacional Experimental del
Táchira, Venezuela
Juan Mantilla, Universidad Nacional Experimental
del Táchira, Venezuela
José Clemente, Universidad Nacional Experimental del
Táchira, Venezuela
Miguel Vera, Universidad de Los Andes, Venezuela
Rubén Medina, Universidad de Los Andes, Venezuela
Chapter fourteen is concerned with the problem of cardiac
motion estimation. A short overview of machine learning
techniques applied to several imaging modalities is
presented. This method is based on the application of
support vector machines (SVM), region growing and a nonrigid
bidimensional correspondence algorithm used for tracking the
anatomical landmarks extracted from the segmented left
ventricle (LV). Some experimental results are presented
and at the end of the chapter a short summary is presented.
Compilation of References ..................................... 323
About the Contributors ........................................ 357
Index ......................................................... 365
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