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Computer-Supported Segmentation of Radiological Data

795

[29]Kotcheff, A. and Taylor, C., Automatic construction of eigenshape models by genetic algorithm, Inform. Process. Med. Imaging, Vol. 1230, pp. 1–14, 1997.

[30]Kelemen, A., Szekely, G., and Gerig, G., Elastic model-based segmentation of 3-d neuroradiological data sets, IEEE Trans. Med. Imaging, Vol. 18, pp. 828–839, 1999.

[31]Staib, L. and Duncan, J., Model-based deformable surface finding for medical images, IEEE Trans. Med. Imaging, Vol. 15, No. 5, pp. 1–12, 1996.

[32]Cootes, T. F., Taylor, C. J., Cooper, D. H., and Graham, J., Active shape models—Their training and application, Comput. Vision Image Understand., Vol. 61, No. 1, pp. 38–59, 1995.

[33]Szekely,´ G., Kelemen, A., Brechbuhler,¨ C., and Gerig, G., Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models, Med. Image Anal., Vol. 1, No. 1, pp. 19–34, 1996.

[34]McInerney, T. and Terzopoulos, D., Deformable models in medical image analysis: A survey, Med. Image Anal., Vol. 1, No. 2, pp. 91–108, 1996.

[35]Cootes, T., Edwards, G., and Taylor, C., Active appearance models, In: Proceedings of the European Conference on Computer Vision, Vol. 2, Springer-Verlag, New-York, pp. 484–498, 1998.

[36]Kelemen, A., Szekely, G., and Gerig, G., Elastic model-based segmentation of 3-d neuroradiological data sets, IEEE Trans. Med. Imaging, Vol. 18, No. 10, pp. 828–839, 1999.

[37]Kruggel, F. and Lohmann, G., Automatical adaption of the stereotactical coordinate system in brain MRI datasets, In: Information Processing in Medical Imaging, Springer-Verlag, New York, pp. 471–476, 1997.

[38]Barrett, W. and Mortensen, E., Interactive live-wire boundary extraction, Medical Image Analysis, pp. 331–341, 1997. Available at citeseer.nj.nec.com/barrett97interactive.html.

796

Cattin et al.

[39]Fischler, M., Tenenbaum, J., and Wolf, H., Detection of roads and linear structures in low-reslution aerial imagery using a multisource knowledge integration technique, Comput. Graph. Image Process., Vol. 15, pp. 201–233, 1981.

[40]O’Donnell, L., Weslin, C.-T., Grimson, W. E. L., Ruiz-alzola, J., Shenton, M. E., and Kikinis, R., Phase-based user-steered image segmentation, In: International Conference on Medical Image Computing and ComputerAssisted Intervention (MICCAI), 2001, pp. 1022–1030.

[41]Falcao,˜ A. and Udapa, J., A 3D generalization of user-steered live-wire segmentation, Med. Image Anal., Vol. 4, No. 1, pp. 389–402, 1997.

[42]Falcao,˜ A., Udapa, J., and Miyazawa, F., An ultra-fast user-steered image segmentation paradigm: Live wire on the fly, IEEE Trans. Med. Imaging, Vol. 19, No. 1, pp. 55–62, 2000.

[43]Haenselmann, T. and Effelsberg, W., Wavelet-based semi-automatic livewire segmentation, SPIE Human Vision and Electronic Imaging VIII, Vol. 5007, pp. 260–269, 2003. Available at citeseer.nj.nec.com/569760.html.

[44]Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models, Int. J. Comput. Vision, Vol. 1, No. 4, pp. 321–331, 1988.

[45]Canny, J., A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, No. 6, pp. 679–698, 1986.

[46]Fua, P. and Leclerc, Y., Model driven edge detection, Mach. Vision Appl., Vol. 3, pp. 45–56, 1990.

[47]Leymarie, F. and Levine, M., Tracking deformable objects in the plane using an active contour model, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 15, No. 6, pp. 617–634, 1993.

[48]Samadani, R., Changes in connectivity in active contour models, In: Proceedings of the IEEE Workshop on Visual Motion, Irvine, California, March 1989, pp. 337–343.

[49]Terzopoulos, D., On matching deformable models to images, Topical Meeting Mach. Vision Tech. Digest Series, Vol. 12, pp. 160–167, 1987.

Computer-Supported Segmentation of Radiological Data

797

[50]Cohen, L. and Cohen, I., A finite element method applied to new active contour models and 3D reconstructions, In: Proceedings of the Third International Conference on Computer Vision, Osaka, Japan, Dec. 1990,

pp.587–591.

[51]Cohen, I., Cohen, L. D., and Ayache, N., Using deformable surfaces to segment 3-D images and infer differential structures, Comput. Vision Graph. Image Process., Vol. 56, No. 2, pp. 242–263, 1992.

[52]Hug, J., Brechbuhler,¨ C., and Szekely,´ G., Tamed snake: A particle system for robust semi-automatic segmentation, In: MICCAI, 1999, pp. 106–115.

[53]Dyn, N., Levin, D., and Gregory, J., A 4-point interpolatory subdivision scheme for curve design, Comput. Aided Geomet. Design, Vol. 4, No. 4,

pp.257–268, 1987.

[54]Hug, J., Semi-Automatic Segmentation of Medical Imagery, Ph.D. Thesis, ETH Zurich¨-Swiss Federal Institute of Technology, 2001.

[55]Dyn, N., Levin, D., and Gregory, J., A butterfly subdivision scheme for surface interpolation with tension control, Trans. Graph., Vol. 9, No. 2,

pp.160–169, 1990.

[56]Zorin, D., Schroder,¨ P., and Sweldens, W., Interpolating subdivision for meshes of arbitrary topology, In: SIGGRAPH, August 1996, pp. 189–192.

[57]Kobbelt, L., Iterative Erzeugung glatter Interpolatoren., Ph.D. Thesis, University at Karlsruhe, 1994.

[58]Schneider, R. and Kobbelt, L., Geometric fairing of irregular meshes for free-form surface design, Comput. Aided Geomet. Design, Vol. 18, No. 4,

pp.359–379, 5 2001.

[59]Desbrun, M., Meyer, M., Schroder, P., and Barr, A., Discrete DifferentialGeometry Operators in nD, preprint, The Caltech Multi-Res Modeling Group, 2000.

[60]Neuenschwander, W., Fua, P., Szekely,´ G., and Kubler,¨ O., Initializing snakes, In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1994, pp. 658–663.

798

Cattin et al.

[61]Hug, J., Brechbuhler,¨ C., and Szekely,´ G., Model-based initialisation for segmentation, In: Proceedings of 6th European Conference on Computer Vision (ECCV 2000), Part II, Vernon, D., ed., Lecture Notes in Computer Science, Springer, Berlin pp. 290–306, 2000.

[62]Harders, M. and Szekely,´ G., Enhancing human computer interaction in medical segmentation, Proc. IEEE, Vol. 91, No. 9, pp. 1430–1442, 2003.

[63]Rosenberg, L., Virtual fixtures: Perceptual tools for telerobotic manipulation, In: IEEE Virtual Reality Annual International Symposium, 1993,

pp.76–82.

[64]Sayers, C. and Paul, R., An operator interface for teleprogramming employing synthetic fixtures, Presence Teleoperat. Virtual Environ., Vol. 3,

pp.309–320, 1994.

[65]Harders, M. and Szekely,´ G., New paradigms for interactive 3D volume segmentation, J. Visual. Comput. Animation, Vol. 13, pp. 85–95, 2002.

[66]Karabassi, E.-A., Papaioannou, G., and Theoharis, T., A fast depth-buffer- based voxelization algorithm, J. Graph. Tools, Vol. 4, No. 4, pp. 5–10, 1999.

The Editors

Dr. Jasjit S. Suri received his BS in computer engineering with distinction from Maulana Azad College of Technology, Bhopal, India, his MS in computer sciences from University of Illinois, Chicago, and Ph.D. in electrical engineering from University of Washington, Seattle. He has been working in the field of computer engineering/imaging sciences for 20 years. He has published more than 125 technical papers in body imaging. He is a lifetime member of research engineering societies: Tau-Beta Pi, Eta-Kappa-Nu, Sigma-Xi, and a member of NY Academy of Sciences, Engineering in Medicine and Biology Society (EMBS), SPIE, ACM, and is also a senior member at IEEE. He is in the editorial board/reviewer of several international journals such as Real Time Imaging, Pattern Analysis and Applications, Engineering in Medicine and Biology, Radiology, Journal of Computer Assisted Tomography, IEEE Transactions of Information Technology in Biomedicine, and IASTED Board.

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He has chaired image processing tracks at several international conferences and has given more than 40 international presentations/seminars. Dr. Suri has written four books in the area of body imaging (such as cardiology, neurology, pathology, mammography, angiography, atherosclerosis imaging) covering medical image segmentation, image and volume registration, and physics of medical imaging modalities like: MRI, CT, X-ray, PET, and ultrasound. He also holds several United States patents. Dr. Suri has been listed in Who’s Who seven times, is a recipient of president’s gold medal in 1980, and has received more than 50 scholarly and extracurricular awards during his career. He is also a Fellow of American Institute of Medical and Biological Engineering (AIMBE) and ABI. Dr. Suri’s major interests are: computer vision, graphics and image processing (CVGIP), object oriented programming, image guided surgery and teleimaging. Dr. Suri had worked for Philips Medical Systems and Siemens Medical Research Divisions. He is also a visiting professor with the department of computer science, University of Exeter, Exeter, England. Currently, Dr. Suri is with JWT Inc. as director of biomedical engineering division (in opthalmology imaging) in conjunction with Biomedical Imaging Laboratories, Case Western Reserve University, Cleveland.

Dr. David Wilson is a professor of biomedical engineering and radiology, Case Western Reserve University. He has research interests in image analysis, quantitative image quality, and molecular imaging, and he has a significant track record of federal research funding in these areas. He has over 60 refereed journal publications and has served as a reviewer for several leading journals. Professor Wilson has six patents and two pending patents in medical imaging. Professor Wilson has been active in the development of international conferences; he was Track Chair at the 2002 EMBS/BMES conference, and he was Technical Program Co-Chair for the 2004 IEEE International Symposium on Biomedical Imaging.

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Professor Wilson teaches courses in biomedical imaging, and biomedical image processing and analysis. He has advised many graduate and undergraduate students, all of whom are quite exceptional, and has been primary research advisor for over 16 graduate students since starting his academic career. Prior to joining CWRU, he worked in X-ray imaging at Siemens Medical Systems at sites in New Jersey and Germany. He obtained his PhD from Rice University. Professor Wilson has actively developed biomedical imaging at CWRU. He has led a faculty recruitment effort, and he has served as PI or has been an active leader on multiple research and equipment developmental awards to CWRU, including an NIH planning grant award for an In Vivo Cellular and Molecular Imaging Center and an Ohio Wright Center of Innovation award. He can be reached at dlw@po.cwru.edu.

Dr. Swamy Laxminarayan currently serves as the chief of biomedical information engineering at the Idaho State University. Previous to this, he held several senior positions both in industry and academia. These have included serving as the chief information officer at the National Louis University, director of the pharmaceutical and health care information services at NextGen Internet (the premier Internet organization that spun off from the NSF sponsored John von Neuman National Supercomputer Center in Princeton), program director of biomedical engineering and research computing and program director of computational biology at the University of Medicine and Dentistry in New Jersey, vice-chair of Advanced Medical Imaging Center, director of clinical computing at the Montefiore Hospital and Medical Center and the Albert Einstein College of Medicine in New York, director of the VocalTec High Tech Corporate University

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in New Jersey, and the director of the Bay Networks Authorized Center in Princeton. He has also served as an adjunct professor of biomedical engineering at the New Jersey Institute of Technology, a clinical associate professor of health informatics, visiting professor at the University of Bruno in Czech Republic, and an honorary professor of health sciences at Tsinghua University in China.

As an educator, researcher, and technologist, Prof. Laxminarayan has been involved in biomedical engineering and information technology applications in medicine and health care for over 25 years and has published over 250 scientific and technical articles in international journals, books, and conferences. His expertize lies in the areas of biomedical information technology, high performance computing, digital signals and image processing, bioinformatics, and physiological systems analysis. He is the coauthor of the book State-of-the-Art PDE and Level Sets Algorithmic Approaches to Static and Motion Imagery Segmentation published by Kluwer Publications and the book Angiography Imaging: State-of-the-Art-Acquisition, Image Processing and Applications Using Magnetic Resonance, Computer Tomography, Ultrasound and X-ray, Emerging Mobile E-Health Systems published by the CRC Press and two volumes of

Handbook of Biomedical Imaging to be published by Kluwer Publications. He has also worked as the editor/coeditor of 20 international conferences and has served as a keynote speaker in international conferences in 13 countries.

He is the founding editor-in-chief and editor emeritus of IEEE Transactions on Information Technology in Biomedicine. He served as an elected member of the administrative and executive committees in the IEEE Engineering in Medicine and Biology Society and as the society’s vice president for 2 years. His other IEEE roles include his appointments as program chair and general conference chair of about 20 EMBS and other IEEE conferences, an elected member of the IEEE Publications and Products Board, member of the IEEE Strategic Planning and Transnational Committees, member of the IEEE Distinguished Lecture Series, delegate to the IEEE USA Committee on Communications and Information Policy (CCIP), U.S. delegate to the European Society for Engineering in Medicine, U.S. delegate to the General Assembly of the IFMBE, IEEE delegate to the Public Policy Commission and the Council of Societies of the AIMBE, fellow of the AIMBE, senior member of IEEE, life member of Romanian Society of Clinical Engineering and Computing, life member of Biomedical Engineering Society of India, and U.S. delegate to IFAC and IMEKO Councils in TC13. He was recently elected to the Administrative Board of the International Federation for

The Editors

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Medical and Biological Engineering, a worldwide organization comprising 48 national members, overseeing global biomedical engineering activities. He was also elected to serve as the publications co-chairman of the Federation.

His contributions to the discipline have earned him numerous national and international awards. He is a fellow of the American Institute of Medical and Biological Engineering, a recipient of the IEEE 3rd Millennium Medal, and a recipient of the Purkynje award from the Czech Academy of Medical Societies, a recipient of the Career Achievement Award, numerous outstanding accomplishment awards, and twice recipient of the IEEE EMBS distinguished service award. He can be reached at s.n.laxminarayan@ieee.org.

Index

Accumulation local moments, 65–67, 102 Active contour model (ACM), 409–410; see also

MRF-based active contour model survey of, 394–399

Acute coronary syndromes (ACS), 458 Adaptative boosting (AdaBoost), 81, 88–91

error rates associated to, 90

Adaptive fuzzy leader clustering (AFLC), 268, 271–273

implementation flow chart, 273 structure, 272

Adaptive weighted model (AWM), 625, 630, 631 Aikaike information criterion (AIC), 147,

150–151 Algebraic closing, 325 Algebraic opening, 325

Arc length, normalized, 494

Arterial vasodilation: see Vasodilation response

Artery, cross section of showing lipids, 451, 452

Artificial intelligence methods (segmentation), 299

Artificial neural network (ANN) classification, 642, 643, 653, 734, 735

Artificial neural networks (ANNs), 599, 600, 602, 617–618, 644, 645, 654; see also Neural networks

Atherosclerosis, regression and progression of, 451–452

Atherosclerotic blood vessel tracking, 405–411

Atherosclerotic plaque, 369–370; see also Carotid artery atherosclerotic plaque analysis

Atherosclerotic plaque segmentation with MCW MR images, 418

Attenuation correction (AC), 162–163 Average separation (AVS) measure, difference

in, 605–606

Average weighted model (AWM) classifier combination, 632–635

Average weighted model (AWM) combination strategy

vs. expert strategy, 639

Average weighted model (AWM) parameter estimation using EM algorithm, 633–634

Average weighted model (AWM) vs. ensemble combination rules, 637–639

Band pass filter network, 720

Basal ganglia, 3-D model of, 763, 764 Bayes classifier, 82

Bezier curve, 500 Bias computation, 494

Bias estimation protocol, 497 Bias-field, 11

Bias-field correction, automated, 14–15, 19–20

examples, 18–19

image model and parameter estimation, 15–18

Bifurcation points (BIF), 337 Binary region labeling process, 481,

482 Bladder-prostate model, 765 Blind spot (optic disk), 340 Blob structures, 534, 538

measures, 536

Blood markers (cardiac disease), 457 Boosting methods, 101, 102 Boundary estimation, 485, 487–492 Box-counting, 68

805