Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy

549 Pages·2018·57.51 MB·English

by  Paulo Mazzoncini de Azevedo-Marques

Medical Image Analysis and
Informatics: Computer-Aided
Diagnosis and Therapy

Medical Image Analysis and
Informatics: Computer-Aided
Diagnosis and Therapy

Edited by
Paulo Mazzoncini de Azevedo-Marques
Arianna Mencattini
Marcello Salmeri
Rangaraj M. Rangayyan
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We dedicate this book
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on computer-aided diagnosis and therapy
for improved health care.
Paulo, Arianna, Marcello, and Raj

Contents
Foreword on CAD: Its Past, Present, and Future ....................................................ix
Kunio Doi
Preface......................................................................................................................xv
Acknowledgment ....................................................................................................xxi
Editors ...................................................................................................................xxiii
Contributors ..........................................................................................................xxv
1 Segmentation and Characterization of White Matter Lesions in FLAIR
Magnetic Resonance Imaging ............................................................................1
Brittany Reiche, Jesse Knight, Alan R. Moody, April Khademi
2 Computer-Aided Diagnosis with Retinal Fundus Images .............................29
Yuji Hatanaka, Hiroshi Fujita
3 Computer-Aided Diagnosis of Retinopathy of Prematurity in Retinal
Fundus Images .................................................................................................57
Faraz Oloumi, Rangaraj M. Rangayyan, Anna L. Ells
4 Automated OCT Segmentation for Images with DME ..................................85
Sohini Roychowdhury, Dara D. Koozekanani, Michael Reinsbach, Keshab K. Parhi
5 Computer-Aided Diagnosis with Dental Images ..........................................103
Chisako Muramatsu, Takeshi Hara, Tatsuro Hayashi, Akitoshi Katsumata, Hiroshi Fujita
6 CAD Tool and Telemedicine for Burns .........................................................129
Begoña Acha-Piñero, José-Antonio Pérez-Carrasco, Carmen Serrano-Gotarredona
7 CAD of Cardiovascular Diseases ...................................................................145
Marco A. Gutierrez, Marina S. Rebelo, Ramon A. Moreno, Anderson G. Santiago,
Maysa M. G. Macedo
8 Realistic Lesion Insertion for Medical Data Augmentation .........................187
Aria Pezeshk, Nicholas Petrick, Berkman Sahiner
9 Diffuse Lung Diseases (Emphysema, Airway and Interstitial Lung Diseases) .....203
Marcel Koenigkam Santos, Oliver Weinheimer
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viii Contents
10 Computerized Detection of Bilateral Asymmetry ........................................219
Arianna Mencattini, Paola Casti, Marcello Salmeri, Rangaraj M. Rangayyan
11 Computer-Aided Diagnosis of Breast Cancer with Tomosynthesis
Imaging. ..........................................................................................................241
Heang-Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski, Jun Wei
12 Computer-Aided Diagnosis of Spinal Abnormalities .................................. 269
Marcello H. Nogueira-Barbosa, Paulo Mazzoncini de Azevedo-Marques
13 CAD of GI Diseases with Capsule Endoscopy ............................................. 285
Yixuan Yuan, Max Q.-H. Meng
14 Texture-Based Computer-Aided Classification of Focal Liver Diseases
using Ultrasound Images .............................................................................. 303
Jitendra Virmani, Vinod Kumar
15 CAD of Dermatological Ulcers (Computational Aspects of CAD for
Image Analysis of Foot and Leg Dermatological Lesions) ........................... 323
Marco Andrey Cipriani Frade, Guilherme Ferreira Caetano, É derson Dorileo
16 In Vivo Bone Imaging with Micro-Computed Tomography .........................335
Steven K. Boyd, Pierre-Yves Lagacé
17 Augmented Statistical Shape Modeling for Orthopedic Surgery and
Rehabilitation ................................................................................................ 369
Bhushan Borotikar, Tinashe Mutsvangwa, Valérie Burdin, Enjie Ghorbel,
Mathieu Lempereur, Sylvain Brochard, Eric Stindel, Christian Roux
18 Disease-Inspired Feature Design for Computer-Aided Diagnosis of
Breast Cancer Digital Pathology Images ......................................................427
Jesse Knight, April Khademi
19 Medical Microwave Imaging and Analysis ...................................................451
Rohit Chandra, Ilangko Balasingham, Huiyuan Zhou, Ram M. Narayanan
20 Making Content-Based Medical Image Retrieval Systems for
Computer-Aided Diagnosis: From Theory to Application .......................... 467
Agma Juci Machado Traina, Marcos Vinícius Naves Bedo, Lucio Fernandes Dutra
Santos, Luiz Olmes Carvalho, Glauco Vítor Pedrosa, Alceu Ferraz Costa, Caetano Traina Jr.
21 Health Informatics for Research Applications of CAD ................................491
Thomas M. Deserno, Peter L. Reichertz
Concluding Remarks ............................................................................................ 505
Paulo Mazzoncini de Azevedo-Marques, Arianna Mencattini, Marcello Salmeri,
Rangaraj Mandayam Rangayyan
Index ......................................................................................................................509
Foreword on CAD:
Its Past, Present, and Future
Computer-aided diagnosis (CAD) has become a routine clinical procedure for detection of breast cancer
on mammograms at many clinics and medical centers in the United States. With CAD, radiologists
use the computer output as a “ second opinion” in making their final decisions. Of the total number
of approximately 38 million mammographic examinations annually in the United States, it has been
estimated that about 80% have been studied with use of CAD. It is likely that CAD is beginning to be
applied widely in the detection and differential diagnosis of many different types of abnormalities in
medical images obtained in various examinations by use of different imaging modalities, including
projection radiography, computed tomography (CT), magnetic resonance imaging (MRI), ultrasonog-
raphy, nuclear medicine imaging, and other optical imaging systems. In fact, CAD has become one of
the major research subjects in medical imaging, diagnostic radiology, and medical physics. Although
early attempts at computerized analysis of medical images were made in the 1960s, serious and system-
atic investigations on CAD began in the 1980s with a fundamental change in the concept for utilization
of the computer output, from automated computer diagnosis to computer-aided diagnosis.
Large-scale and systematic research on and development of various CAD schemes was begun by us in
the early 1980s at the Kurt Rossmann Laboratories for Radiologic Image Research in the Department of
Radiology at the University of Chicago. Prior to that time, we had been engaged in basic research related
to the effects of digital images on radiologic diagnosis, and many investigators had become involved
in research and development of a picture archiving and communication system (PACS). Although it
seemed that PACS would be useful in the management of radiologic images in radiology departments
and might be beneficial economically to hospitals, it looked unlikely at that time that PACS would bring
a significant clinical benefit to radiologists. Therefore, we thought that a major benefit of digital images
must be realized in radiologists’ daily work of image reading and radiologic diagnosis. Thus, we came to
the concept of computer-aided diagnosis.
In the 1980s, the concept of automated diagnosis or automated computer diagnosis was already known
from studies performed in the 1960s and 1970s. At that time, it was assumed that computers could
replace radiologists in detecting abnormalities, because computers and machines are better at perform-
ing certain tasks than human beings. These early attempts were not successful because computers were
not powerful enough, advanced image processing techniques were not available, and digital images were
not easily accessible. However, a serious flaw was an excessively high expectation from computers. Thus,
it appeared to be extremely difficult at that time to carry out a computer analysis of medical images. It
was uncertain whether the development of CAD schemes would be successful or would fail. Therefore,
we selected research subjects related to cardiovascular diseases, lung cancer, and breast cancer, includ-
ing for detection and/or quantitative analysis of lesions involved in vascular imaging, as studied by H.
Fujita and K.R. Hoffmann; detection of lung nodules in chest radiographs by M.L. Giger; and detection
of clustered microcalcifications in mammograms by H.P. Chan.
ix