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Lung Imaging and CADx

Edited by: Ayman El-Baz , Jasjit S. Suri

Print publication date:  May  2019
Online publication date:  April  2019

Print ISBN: 9781138050914
eBook ISBN: 9780429055959
Adobe ISBN:

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Book description

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.

Table of contents

Prelims Download PDF
Chapter  1:  Computer-Aided Diagnosis of Chronic Obstructive Pulmonary Disease Using Accurate Lung Air Volume Estimation in Computed Tomographic Imaging Download PDF
Chapter  2:  Early Detection of Chronic Obstructive Pulmonary Disease: Influence on Lung Cancer Epidemiology Download PDF
Chapter  3:  Dual Energy Computed Tomography for Lung Cancer Diagnosis and Characterization Download PDF
Chapter  4:  X-Ray Dark-Field Imaging of Lung Cancer in Mice Download PDF
Chapter  5:  Lung Cancer Screening Using Low-Dose Computed Tomography Download PDF
Chapter  6:  Computer-Aided Diagnosis of Lung Nodules: Systems for Estimation of Lung Cancer Probability and False-Positive Reduction of Lung Nodule Detection Download PDF
Chapter  7:  Automated Lung Cancer Detection From PET/CT Images Using Texture and Fractal Descriptors Download PDF
Chapter  8:  Lung Cancer Risk of a Population Exposed to Airborne Particles: The Contribution of Different Activities and Microenvironments Download PDF
Chapter  9:  Lung Nodule Classification Basedon the Integration of a Higher-Order Markov-Gibbs Random Field Appearance Model and Geometric Features Download PDF
Chapter  10:  Smoking Cessation and Lung Cancer Screening Programs: The Rationale and Method to Integration Download PDF
Chapter  11:  Automatic Lung Segmentation and Interobserver Variability Analysis Download PDF
Chapter  12:  Classification of Diseased Lungs Using a Combination of Riesz and Gabor Transforms and Machine Learning Download PDF
Chapter  13:  An Unsupervised Parametric MixtureModel for Automatic Three-DimensionalLung Segmentation Download PDF
Chapter  14:  How Deep Learning Is Changing the Landscape of Lung Cancer Diagnosis Download PDF
Chapter  15:  Early Assessment of Radiation-Induced Lung Injury Download PDF
Index Download PDF
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