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Image-Processing Techniques
for Tumor Detection
edited by
Robin N. Strickland
University of Arizona
Tucson, Arizona
ISBN: 0-8247-0637-4
This book is printed on acid-free paper.
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Current printing (last digit):
10987654321
PRINTED IN THE UNITED STATES OF AMERICA
Preface
The urgent need for computer-assisted detection of tumors and lesions in medical
images becomes clear when one considers the state of affairs in X-ray film mam-
mography for breast cancer screening. In the United States it is estimated that
there are currently more than 50 million women over the age of 40 at risk of con-
tracting breast cancer. If only one-half of these women are screened by mammo-
graphic examination each year—and given that in the United States each breast is
viewed from two angles—then the number of X-ray films that must be read by ra-
diologists reaches the staggering total of 100 million per annum. A recent report
estimating that somewhere between 10 and 30% of breast lesions are missed dur-
ing routine screening is therefore not surprising. Motivated by such alarming
statistics, researchers are developing computer image processing algorithms for
detecting signs of disease. Their goal is to develop screening systems that will pro-
vide a second opinion, to prompt the radiologist to take a second look at a suspi-
cious region in the image. Besides mammography, there exists a need for image
processing techniques to assist the radiologist in reading chest X-rays, MRI scans,
and nuclear medical images. These modalities share the property with mammog-
raphy that signs of disease are often extremely subtle.
This book is the first ever devoted to the subject of tumor detection using
image processing techniques. Many of the chapters discuss image processing al-
gorithms that analyze a specific type of medical image and point to suspected tu-
mors. These are the so-called computer-aided diagnosis (CAD) techniques that act
 as second-opinion providers. Also represented are image enhancement algorithms
that make it more likely that a radiologist will see the tumor. Such algorithms do
not perform tumor detection directly, but they certainly can improve detectability.
The book contains 14 chapters arranged into two parts. The first (
Chapters
1
–
5
) discusses the background behind diagnostic imaging, image processing
methods and their evaluation, and clinical applications. The second part (
Chapters
6
–
14
) examines current tumor detection techniques in mammography, chest X-
ray, MRI imaging, and nuclear medicine.
This book summarizes the state of the art in image processing methods for
detecting tumors in medical images. The intended audience includes electrical en-
gineers, computer scientists, radiologists, and other researchers in the health sci-
ences.
Robin N. Strickland
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