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Provides a comprehensive overview of machine learning and deep
learning techniques for biomedical imaging Includes thoracic
imaging, abdominal imaging, brain imaging, and retinal imaging
Covers new and emerging methods in machine learning Features
contributions from leading experts Presents tools to improve
computer aided diagnosis
Today's healthcare organizations must focus on a lot more than just
the health of their clients. The infrastructure it takes to support
clinical-care delivery continues to expand, with information
technology being one of the most significant contributors to that
growth. As companies have become more dependent on technology for
their clinical, administrative, and financial functions, their IT
departments and expenditures have had to scale quickly to keep up.
However, as technology demands have increased, so have the options
for reliable infrastructure for IT applications and data storage.
The one that has taken center stage over the past few years is
cloud computing. Healthcare researchers are moving their efforts to
the cloud because they need adequate resources to process, store,
exchange, and use large quantities of medical data. Cloud Computing
in Medical Imaging covers the state-of-the-art techniques for cloud
computing in medical imaging, healthcare technologies, and
services. The book focuses on Machine-learning algorithms for
health data security Fog computing in IoT-based health care Medical
imaging and healthcare applications using fog IoT networks
Diagnostic imaging and associated services Image steganography for
medical informatics This book aims to help advance scientific
research within the broad field of cloud computing in medical
imaging, healthcare technologies, and services. It focuses on major
trends and challenges in this area and presents work aimed to
identify new techniques and their use in biomedical analysis.
Stochastic Modeling for Medical Image Analysis provides a brief
introduction to medical imaging, stochastic modeling, and
model-guided image analysis. Today, image-guided computer-assisted
diagnostics (CAD) faces two basic challenging problems. The first
is the computationally feasible and accurate modeling of images
from different modalities to obtain clinically useful information.
The second is the accurate and fast inferring of meaningful and
clinically valid CAD decisions and/or predictions on the basis of
model-guided image analysis. To help address this, this book
details original stochastic appearance and shape models with
computationally feasible and efficient learning techniques for
improving the performance of object detection, segmentation,
alignment, and analysis in a number of important CAD applications.
The book demonstrates accurate descriptions of visual appearances
and shapes of the goal objects and their background to help solve a
number of important and challenging CAD problems. The models focus
on the first-order marginals of pixel/voxel-wise signals and
second- or higher-order Markov-Gibbs random fields of these signals
and/or labels of regions supporting the goal objects in the
lattice. This valuable resource presents the latest state of the
art in stochastic modeling for medical image analysis while
incorporating fully tested experimental results throughout.
There is an urgent need to develop and integrate new statistical,
mathematical, visualization, and computational models with the
ability to analyze Big Data in order to retrieve useful information
to aid clinicians in accurately diagnosing and treating patients.
The main focus of this book is to review and summarize
state-of-the-art big data and deep learning approaches to analyze
and integrate multiple data types for the creation of a decision
matrix to aid clinicians in the early diagnosis and identification
of high risk patients for human diseases and disorders. Leading
researchers will contribute original research book chapters
analyzing efforts to solve these important problems.
Provides a comprehensive overview of machine learning and deep
learning techniques for biomedical imaging Includes thoracic
imaging, abdominal imaging, brain imaging, and retinal imaging
Covers new and emerging methods in machine learning Features
contributions from leading experts Presents tools to improve
computer aided diagnosis
As one of the most important tasks in biomedical imaging, image
segmentation provides the foundation for quantitative reasoning and
diagnostic techniques. A large variety of different imaging
techniques, each with its own physical principle and
characteristics (e.g., noise modeling), often requires
modality-specific algorithmic treatment. In recent years,
substantial progress has been made to biomedical image
segmentation. Biomedical image segmentation is characterized by
several specific factors. This book presents an overview of the
advanced segmentation algorithms and their applications.
Developing an effective computer-aided diagnosis (CAD) system for
lung cancer is of great clinical importance and can significantly
increase the patient's chance for survival. For this reason, CAD
systems for lung cancer have been investigated in a large number of
research studies. A typical CAD system for lung cancer diagnosis is
composed of four main processing steps: segmentation of the lung
fields, detection of nodules inside the lung fields, segmentation
of the detected nodules, and diagnosis of the nodules as benign or
malignant. This book overviews the current state-of-the-art
techniques that have been developed to implement each of these CAD
processing steps. Overviews the latest state-of-the-art diagnostic
CAD systems for lung cancer imaging and diagnosis Offers detailed
coverage of 3D and 4D image segmentation Illustrates unique fully
automated detection systems coupled with 4D Computed Tomography
(CT) Written by authors who are world-class researchers in the
biomedical imaging sciences Includes extensive references at the
end of each chapter to enhance further study Ayman El-Baz is a
professor, university scholar, and chair of the Bioengineering
Department at the University of Louisville, Louisville, Kentucky.
He earned his bachelor's and master's degrees in electrical
engineering in 1997 and 2001, respectively. He earned his doctoral
degree in electrical engineering from the University of Louisville
in 2006. In 2009, he was named a Coulter Fellow for his
contributions to the field of biomedical translational research. He
has 17 years of hands-on experience in the fields of bio-imaging
modeling and noninvasive computer-assisted diagnosis systems. He
has authored or coauthored more than 500 technical articles (132
journals, 23 books, 57 book chapters, 211 refereed-conference
papers, 137 abstracts, and 27 U.S. patents and disclosures). Jasjit
S. Suri is an innovator, scientist, a visionary, an industrialist,
and an internationally known world leader in biomedical
engineering. He has spent over 25 years in the field of biomedical
engineering/devices and its management. He received his doctorate
from the University of Washington, Seattle, and his business
management sciences degree from Weatherhead School of Management,
Case Western Reserve University, Cleveland, Ohio. He was awarded
the President's Gold Medal in 1980 and named a Fellow of the
American Institute of Medical and Biological Engineering for his
outstanding contributions in 2004. In 2018, he was awarded the
Marquis Life Time Achievement Award for his outstanding
contributions and dedication to medical imaging and its management.
This book covers state-of-the-art medical image analysis approaches
currently pursued in autism research. Chapters cover recent
advances in diagnosis using structural neuroimaging. All aspects of
imaging are included, such as electrophysiology (EEG, ERP, QEEG,
and MEG), postmortem techniques, and advantages and difficulties of
depositing/acquiring images in larger databases. The book
incorporates 2D, 3D, and 4D imaging and advances scientific
research within the broad field of autism imaging.
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Lung Cancer and Imaging (Hardcover)
Ayman El-Baz, Jasjit Suri; Contributions by Ahmed Shaffie, Ahmed Soliman, Ali Mahmoud, …
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R3,442
Discovery Miles 34 420
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Ships in 12 - 17 working days
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This book covers the state-of-the-art approaches for automated
non-invasive systems for early cardiovascular disease diagnosis. It
includes several prominent imaging modalities such as MRI, CT, and
PET technologies. There is a special emphasis placed on automated
imaging analysis techniques, which are important to biomedical
imaging analysis of the cardiovascular system. Novel 4D based
approach is a unique characteristic of this product. This is a
comprehensive multi-contributed reference work that will detail the
latest developments in spatial, temporal, and functional cardiac
imaging. The main aim of this book is to help advance scientific
research within the broad field of early detection of
cardiovascular disease. This book focuses on major trends and
challenges in this area, and it presents work aimed to identify new
techniques and their use in biomedical image analysis. Key
Features: Includes state-of-the art 4D cardiac image analysis
Explores the aspect of automated segmentation of cardiac CT and MR
images utilizing both 3D and 4D techniques Provides a novel
procedure for improving full-cardiac strain estimation in 3D image
appearance characteristics Includes extensive references at the end
of each chapter to enhance further study
Lung cancer remains the leading cause of cancer-related deaths
worldwide. Early diagnosis can improve the effectiveness of
treatment and increase a patient's chances of survival. Thus, there
is an urgent need for new technology to diagnose small, malignant
lung nodules early as well as large nodules located away from large
diameter airways because the current technology-namely, needle
biopsy and bronchoscopy-fail to diagnose those cases. However, the
analysis of small, indeterminate lung masses is fraught with many
technical difficulties. Often patients must be followed for years
with serial CT scans in order to establish a diagnosis, but
inter-scan variability, slice selection artifacts, differences in
degree of inspiration, and scan angles can make comparing serial
scans unreliable. Lung Imaging and Computer Aided Diagnosis brings
together researchers in pulmonary image analysis to present
state-of-the-art image processing techniques for detecting and
diagnosing lung cancer at an early stage. The book addresses
variables and discrepancies in scans and proposes ways of
evaluating small lung masses more consistently to allow for more
accurate measurement of growth rates and analysis of shape and
appearance of the detected lung nodules. Dealing with all aspects
of image analysis of the data, this book examines: Lung
segmentation Nodule segmentation Vessels segmentation Airways
segmentation Lung registration Detection of lung nodules Diagnosis
of detected lung nodules Shape and appearance analysis of lung
nodules Contributors also explore the effective use of these
methodologies for diagnosis and therapy in clinical applications.
Arguably the first book of its kind to address and evaluate
image-based diagnostic approaches for the early diagnosis of lung
cancer, Lung Imaging and Computer Aided Diagnosis constitutes a
valuable resource for biomedical engineers, researchers, and
clinicians in lung disease imaging.
Lung cancer remains the leading cause of cancer-related deaths
worldwide. Early diagnosis can improve the effectiveness of
treatment and increase a patient's chances of survival. Thus, there
is an urgent need for new technology to diagnose small, malignant
lung nodules early as well as large nodules located away from large
diameter airways because the current technology-namely, needle
biopsy and bronchoscopy-fail to diagnose those cases. However, the
analysis of small, indeterminate lung masses is fraught with many
technical difficulties. Often patients must be followed for years
with serial CT scans in order to establish a diagnosis, but
inter-scan variability, slice selection artifacts, differences in
degree of inspiration, and scan angles can make comparing serial
scans unreliable. Lung Imaging and Computer Aided Diagnosis brings
together researchers in pulmonary image analysis to present
state-of-the-art image processing techniques for detecting and
diagnosing lung cancer at an early stage. The book addresses
variables and discrepancies in scans and proposes ways of
evaluating small lung masses more consistently to allow for more
accurate measurement of growth rates and analysis of shape and
appearance of the detected lung nodules. Dealing with all aspects
of image analysis of the data, this book examines: Lung
segmentation Nodule segmentation Vessels segmentation Airways
segmentation Lung registration Detection of lung nodules Diagnosis
of detected lung nodules Shape and appearance analysis of lung
nodules Contributors also explore the effective use of these
methodologies for diagnosis and therapy in clinical applications.
Arguably the first book of its kind to address and evaluate
image-based diagnostic approaches for the early diagnosis of lung
cancer, Lung Imaging and Computer Aided Diagnosis constitutes a
valuable resource for biomedical engineers, researchers, and
clinicians in lung disease imaging.
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