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 Proceedings of the First International Workshop on Computer-Aided Diagnosis, Chicago, 20-23 September 1998
Edited by
K. Doi, The University of Chicago, Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, MC2026, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
H. MacMahon, The University of Chicago, Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, MC2026, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
M.L. Giger, The University of Chicago, Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, MC2026, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
K.R. Hoffmann, The University of Chicago, Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, MC2026, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
Included in series
International Congress, 1182
Description
Over the last decade or so, many investigators have carried out basic studies and clinical applications toward the development of modern
computerized schemes for detection and characterization of lesions in radiologic images, based on computer vision and artificial intelligence.
These methods and techniques are generally called computer-aided diagnosis (CAD) schemes. The development of CAD has now reached a new
phase, since the first commercial unit of detection of breast lesion in mammograms was approved in June 1998 by the FDA for marketing
and sale for clinical use.
This book, Computer-Aided Diagnosis in Medical Imaging, presents papers from the First International
Workshop on Computer-Aided Diagnosis held on September, 1998 at the University of Chicago Downtown Center. The meeting provided a forum
for leading researchers and practitioners in this rapidly expanding field, encompassing automated image analysis, quantitation of image
information, 2D and 3D multimodality image integration, advanced image processing and artificial neural network application. Advances
have been made in the computerized analysis of digital chest images, especially the detection of pulmonary nodules, using such techniques
as artificial neural networks, temporal subtraction, and dual-energy imaging. Various observer performance studies have documented the
benefit of radiologists using a computer aid in their interpretation process. Similar strides have been made with breast imaging with
the aims of increased patient management. CAD research in breast imaging is now including digital mammography, ultrasound, and magnetic
resonance imaging.
Computerized enhancement, analysis and visualization of three-dimensional medical images have touched both diagnostic
radiology (e.g., enhanced interpretation) and radiation therapy (e.g., treatment planning). As low-dose, spiral CT becomes routine, CT
images may potentially be used for the screening of disease such as lung cancer utilizing computerized detection of pulmonary nodules
in CT images of the thorax. Image segmentation and visualization techniques are being investigated as means to view representations
of cardiac and abdominal structures such as in virtual colonoscopy. Vascular imaging based on either biplane, CTA, and intravascular
ultrasound will benefit greatly with developed computerized methods for fusion and visualization.
The efficient and effective use
of CAD will depend on well implemented PACS, which will transport images, patient data, and CAD results to required sites within and
about medical centers. It is evident from the Workshop that the future of computer-aided diagnosis is more promising now than ever. Continued
research for improved computer image analysis methods and future clinical trials will help optimize systems, as well as determine their
actual contributions to the interpretation process.
Audience
Researchers in image analysis (industry & academic), Bioengineers, Radiologists, Medical Physicists, Computer Scientists and Engineers
Contents
Keynote Address and Overview.
Opportunities in Medical Imaging
(W.R. Hendee).
Computer-aided diagnosis and its potential impact
on diagnostic radiology
(K. Doi).
CAD for Chest Imaging.
Clinical application of CAD in the chest
(H. MacMahon).
Computerized
analysis of chest CT images
(J. Toriwaki, A. Shimizu, K. Mori).
Computer-aided diagnosis of pulmonary nodules in chest radiographs:
distinction of nodules from false positives based on wavelet snake and articial neural network
(H. Yoshida, B. Keserci, K. Doi).
Application
of temporal subtraction to screening chest radiographs with a mobile computed radiography system
(S. Katsuragawa et al.).
Pattern
recognition technique for chest CAD system
(T. Hara et al.).
Detection of lung nodule on digital energy subtracted soft-tissue
and conventional chest images from a CR system
(X.-W. Xu, H. MacMahon, K. Doi).
Implementation of a technique for performing real-time
ROC oberser studies to examine the effects of CAD schemes on the performance of radiologists
(R. Engelmann et al.).
Computer-aided
diagnosis for interstitial infiltrates on chest radiographs: new physical measures using gray-level run length analysis
(J. Morishita,
S. Katsuragawa, K. Doi).
Reduction of false positives in computer diagnosis of chest X-ray images using interval change detection between
two images
(A. Shimizu et al.).
Automated abnormal asymmetry detection in digital posteroanterior chest radiographs
(S.G. Armato
III, M.L. Giger, H. MacMahon).
Stochastic and deterministic texture modeling and analysis for image processing in chest radiography
(R. Vargas-Voracek, C.E. Floyd Jr.).
Computer-aided techniques to characterize solitary pulmonary nodules imaged on CT
(M.F. McNitt-Gray et al.).
A procedure for automated assignment of anatomical names of bronchial branches extracted from 3-D X-ray CT images
and its application to virtualized endoscope system
(K. Mori et al.).
Knowledge-based method for segmentation and quantitative
analysis of lung function from CT
(M.S. Brown et al.).
Computerized detection of lung nodules in computed tomography scans
(S.G. Armato III et al.).
Computer-aided diagnosis system for lung cancer screening by CT
(H. Jiang et al.).
Computer
aided diagnostic system for pulmonary nodules based on helical CT images
(K. Kanazawa et al.).
Curvature based approach for
computer-aided diagnosis of pulmonary nodules using thin-section CT images
(Y. Kawata et al.).
Pulmonary structure analysis
based on thoracic thin-section CT images and its application
(T. Tozaki et al.).
Detection algorithm of interval change using
3D thoracic images
(M. Shimazu et al.).
Algorithm for lobar extraction using linear feature detector
(M. Kubo et al.).
The surface-shape operator: a pre-processing for image analysis
(P. Sukanya, T. Takamatsu, M. Sato).
CAD for Breast Imaging.
Overview of computer-aided diagnosis in breast imaging
(M.L. Giger).
The role of CAD in mammography and missed lesions
(R.A. Schmidt).
Initial clinical experience with CAD in mammography
(H. Sittek, M.F. Reiser).
Prediction of breast biopsy outcomes from mammographic
findings
(C.E. Floyd Jr., J.Y. Lo, J.A. Baker).
Clinical results with the R2 ImageChecker™ Mammographic CAD system
(T. Doi et al.).
Prospective testing of a clinical CAD workstation for the detection of breast lesions on mammograms
(R.M.
Nishikawa et al.).
A visualized mammographic database in computer-aided diagnosis
(J. Sklansky et al.).
Computer-aided
diagnosis of breast cancer
(J.Y. Lo, C.E. Floyd).
Development of a mammogram CAD system: performance studies with large databases
(H.
Fujita et al.).
Improvement in radiologists' diagnosis of malignant and benign clustered microcalcifications by the use of
computer-aided diagnosis (CAD)
(Y. Jiang et al.).
Unsupervised detection and delineation of fine and subtle microcalcifications
in high resolution X-ray mammographic images
(M. Bruynooghe).
Study on the effect of sub-pixel interpolation on the detection of microcalcifications
(D. Meersman, P. Scheunders, D. Van Dyck).
Computer-aided diagnosis for detection of clustered microcalcifications in mammograms: automated
optimization of performance based on genetic algorithm
(H. Yoshida et al.).
Adaptive directional wavelet-base CAD method for
mass detection
(W. Qian, L. Li, L.P. Clarke).
Computer-aided mammogram screening: identification of regions of interest
(P. Bakic, D.
Brzakovic).
Characterization of breast masses using texture and shape features
(S.K. Kinoshita et al.).
A set of texture features
to differentiate between mass and normal breast tissue on digital mammograms
(C. Varela et al.).
Robustness of a computerized
scheme for the classification of malignant and benign masses on digitized mammograms
(Z. Huo, M.L. Giger).
Characterization of breast
cancer using statistical approaches
(R.J. Ferrari et al.).
Visualization of mammograms via fusion of enhanced features
(I.
Koren et al.).
DOCTOUR: A comprehensive toolset for enhanced visualization and computer-aided detection of lesions in mammograms
(S.R. Nelson, S.M. Tuovila, C.M. Smith).
A tool for mammography: CALMA
(S.R. Amendolia et al.).
A method for computerized assessment
of tumor extent in contrast-enhanced MR images of the breast
(K.G.A. Gilhuijs, M.L. Giger, U. Bick).
CAD for Vascular Imaging.
Overview of CAD in the vascular system
(K.R. Hoffmann).
Future of computerized analysis of vascular images
(J.H.C. Reiber et al.).
Computer assisted diagnosis (CAD) of the vascular system using tomographic techniques
(E.K. Fishman).
Advanced analysis of ultrasound
cardiac images
(D.J. Skorton).
CAD of cranial vessels in CT angiographic studies
(M. Fiebich et al.).
Computer assisted diagnosis
system for coronary calcification based on helical CT image
(Y. Ukai et al.).
3D orientations of catheters from single projections
(J. Esthappan, K.R. Hoffmann).
A comprehensive method for geometrically correct 3-D reconstruction of coronary arteries by fusion of
intravascular ultrasound and biplane angiography
(A. Wahle et al.).
Analysis of 3D pulmonary microangiograms
(R.H. Johnson et al.).
Volume rendering image segmentation for cerebral arteriovenous malformations
(A.B. Jani et al.).
A study
on tracking angiographic vascular images
(A. Sen et al.).
CAD of viral progression in CMV retinitis by the comparison of handoutlined
segmentations on series of angiograms
(D. Brahmi et al.).
CAD for Musculoskeletal, Gastrointestinal, and Nuclear Imaging.
Evaluation of manual and computerized radiographic techniques for analyzing wear in total hip arthroplasty based on repeatability analysis
and autopsy retrievals
(J.M. Martell).
Computerized analysis of radiographic bone patterns
(M.R. Chinander et al.).
Automated
extraction of cancer lesions from double contrast x-ray images of stomach
(Y. Mekada et al.).
CAD for 3D Imaging.
Future directions in 3D computer aided diagnosis and therapy
(M.W. Vannier, J.W. Haller, K.R. Smith).
New directions in computer-based
diagnostic imaging
(K.H. Höhne et al.).
Quantitative analysis of CT liver images
(D. Selle et al.).
Virtual
colonoscopy with computer-assisted polyp detection
(D.J. Vining et al.).
Image fusion and multimodality 3D imaging
(C.A. Pelizzari).
New visualization techniques for virtual colonoscopy: methods and evaluation
(S. Napel et al.).
Workstation for the computer
aided diagnosis of colon cancer
(Y. Samara et al.).
A method for extracting of 3-D organ region from CT data using deformable
model
(T. Abe, M. Minami).
Multiscale isomaxima representation of anatomical structures in CT
(M. Shim, A. Laine, D. Benn).
CAD
and PACS.
CAD in PACS environment
(H.K. Huang, F. Cao, E. Pietka).
Security of workstations in medical tele-imaging
(H.U. Lemke).
Information infrastructure for CAD
(F.M. Behlen).
Future implications of CAD with PACS
(M. Kormano).
Implementing CAD in DICOM PACS
environments
(F.M. Behlen).
CAD and Evaluations.
Variation in diagnostic accuracy: potential role of computer-aided diagnosis
(E.J. Potchen).
Image quality issues for computer-aided diagnosis
(R.F. Wagner et al.).
Evaluation of CAD methods
(C.E. Metz).
Finite-Sample dependence of classifier assessment in Computer-Aided-Diagnosis
(R.F. Wagner et al.).
Index of authors.
| Bibliographic details |
Hardbound, 580 pages, publication date: JUL-1999
ISBN-13: 978-0-444-50058-8
ISBN-10: 0-444-50058-8
Imprint: EXCERPTA MEDICA
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