Bibliography

[1] Jemal, A., Thomas, A., and Murray, T., Cancer statistics, 2002, CA Cancer J. Clin., Vol. 52, pp. 23-47, 2002.

[2] Kern, S., Tempero, M., and Conley, B., (Co-Chairs), Pancreatic cancer: An agenda for action, Report of the Pancreatic Cancer Progress Group, National Cancer Institute, February 2001.

[3] Kuvshinoff, B. W. and Bryer, M. P., Treatment of resectable and locally advanced pancreatic cancer, Cancer Control, Vol. 7, No. 5, pp. 428-436, 2000.

[4] Lin, Y., Tamakoshi, A., Kawamura, T., Inaba, Y., Kikuchi, S., Motohashi, Y., Kurosawa, M., and Ohno, Y., An epidemiological overview of environmental and genetic risk factors of pancreatic cancer, Asian Pacific J. Cancer Prev., Vol. 2, pp. 271-280, 2001.

[5] Li, D. and Jiao, L., Molecular epidemiology of pancreatic cancer, Int. J. Gastrointest. Cancer, Vol. 33, No. 1, pp. 3-14, 2003.

[6] Ghadirian, P., Lynch, H. T., and Krewski, D., Epidemiology of pancreatic cancer: an overview, Cancer Detect Prev., Vol. 27, No. 2, pp. 87-93, 2003.

[7] Van Hoe, L. and Baert, A. L., Pancreatic carcinoma: Applications of helical computed tomography, Endoscopy, Vol. 29, pp. 539-560, 1997.

[8] Yeo, T. P., Hruban, R. H., Leach, S. D., Wilentz, R. E., Sohn, T. A., Kern, D. E., Iacobuzio-Donahue, C. A., Maitra, A., Goggins, M., Canto, M. I., Abrams, R. A., Laheru, D., Jaffee, E. M., Hidalgo, M., and Yeo, C. J., Pancreatic cancer, Curr. Prob. Cancer, Vol. 26, No. 4, pp. 176-275, 2002.

[9] Tamm, E. P., Silverman, P. M., Charnsangavej, C., and Evans, D. B., Diagnosis, staging, and surveillance of pancreatic cancer, AJR, Vol. 180, pp. 1311-1323, 2003.

[10] Clark, L. R., Jaffe, M. H., Choyke, P. L., Grant, E. G., and Zeman, R. K., Pancreatic imaging, Radiol. Clin. North Am., Vol. 23, No. 3, pp. 489-501, 1985.

[11] Haaga, J. R., Alfide, R. J., Zelch, M. G., Meany, T. F., Boller, M., Gonzalez, L., andJelden, G. L., Computed tomography of the pancreas, Radiology, Vol. 120, pp. 589-595, 1976.

[12] Haaga, J. R., Alfide, R. J., Harvilla, T. R., Tubbs, R., Gonzalez, L., Meany, T. F., and Corsi, M. A., Definitive role of CT scanning of the pancreas: The second year's experience, Radiology, Vol. 124, pp. 723-730, 1977.

[13] Sheth, S., Hruban, R. K., and Fishman, E. K., Helical CT of islet cell tumors of the pancreas: Typical and atypical manifestations, AJR, Vol. 179, pp. 725-730, 2002.

[14] Horton, K. M. and Fishman, E. K., Adenocarcinoma of the pancreas: CT imaging, Radiol. Clin. North Am., Vol. 40, pp. 1263-1272, 2002.

[15] Horton, K. M., Multidetector CT and three-dimensional imaging of the pancreas: state of the art, J. Gastrointest. Surg., Vol. 6, pp. 126-128, 2002.

[16] Winston, C. B., Mitchell, D. G., Outwater, E. K., and Ehrlich, S. M., Pancreatic signal intensity on T1-weighted fat saturation MR images: Clinical correlation, J. Magn. Reson. Imaging, Vol. 5, pp. 267-271, 1995.

[17] Ragozzino, A. and Scaglione, M., Pancreatic head mass: What can be done? Diagnosis: Magnetic resonance imaging, J. Pancreas, Vol. 1, pp. 100-107, 2000.

[18] Barish, M. A., Yucel, E. K., and Ferrucci, J. T., Magnetic resonance cholangiopancreatography, NEJM, Vol. 341, pp. 258-264, 1999.

[19] Fulcher, A. S. and Turner, M. A., MR pancreatography: A useful tool for evaluating pancreatic disorders, Radiographics, Vol. 19, pp. 5-24,

1999.

[20] Adamek, H. E., Albert, J., Breer, H., Weitz, M., Schilling, D., and Riemann, J. F., Pancreatic cancer detection with magnetic resonance cholan-giopancreatography and endoscopic retrograde cholangiopancreatog-raphy: a prospective controlled study, Lancet, Vol. 356, pp. 190-193,

2000.

[21] Mertz, H. R., Sechopoulos, P., Delbeke, D., and Leach, S. D., EUS, PET, and CT scanning for evaluation of pancreatic adenocarcinoma, Gastrointest. Endosc., Vol. 52, pp. 367-371, 2000.

[22] Wiersema, M. J., Accuracy of endoscopic ultrasound in diagnosing and staging pancreatic carcinoma, Pancreatology, Vol. 1, pp. 625-632, 2001.

[23] Kalra, M. K., Maher, M. M., Boland, G. W., Saini, S., and Fischman, A. J., Correlation of positron emission tomography and CT in evaluating pancreatic tumors: Technical and clinical implications, AJR, Vol. 181, No. 2, pp. 387-393, 2003.

[24] Koyama, K., Okamura, T., Kawabe, J., Nakata, B., Hirakawa-Chung, K. Y. S., Ochi, H., and Yamada, R., Diagnostic usefulness of FDG PET for pancreatic mass lesions, Ann. Nuclear Med., Vol. 15, No. 3, pp. 217-224, 2001.

[25] Dupuy, D. E., Costello, P., and Ecker, C. P., Spiral CT of the pancreas, Radiology, Vol. 183, pp. 815-818, 1992.

[26] DiChiro, G. and Brooks, R. A., The 1979 Nobel prize in physiology and medicine, Science, Vol. 206, No. 30, pp. 1060-1062, 1979.

[27] Kalender, W. A. and Polacin, A., Physical performance characteristics of spiral CT scanning, Med. Phys., Vol. 18, No. 5, pp. 910-915, 1991.

[28] Boone, J. M., Computed tomography: Technology update on multiple detector array scanners and PACS considerations, In: Practical Digital Imaging and PACS, Seibert, J. A., Filipow L. J., and Andriole, K. P., eds., AAPM Medical Physics Monograph No. 25, Medical Physics Publishing, Madison, WI, pp. 37-65, 1999.

[29] Swindell, W. and Webb, S., X-ray transmission computed tomography, In: The Physics of Medical Imaging, Webb, S., ed., Adam Hilger, Bristol, pp. 98-127, 1988.

[30] McCollough, C. H. and Zink, F. E., Performance evaluation of a multi-slice CT system, Med. Phys., Vol. 26, No. 11, pp. 2223-2230, 1999.

[31] Sheedy, P. F., II., Stephens, D. H., Hattery, R. R., MacCarty, R. L., and Williamson, B., Jr., Computer tomography of the pancreas, Radiol. Clin. North Am., Vol. 15, No. 3, pp. 349-366, 1977.

[32] Dendy, P. P. and Heaton, B., Physics for Diagnostic Radiolog, 2nd ed., Medical Science Series, Institute of Physics Publishing, Bristol, 1999.

[33] Remer, E. M. and Baker, M. E., Imaging of chronic pancreatitis, Radiol. Clin. North Am., Vol. 40, pp. 1229-1242, 2002.

[34] Love, L., (guest ed.), Symposium on abdominal imaging, Radiol. Clin. North Am., Vol. 17, No. 1, 1979.

[35] Frank Miller, H., (guest ed.), Radiology of the pancreas, gallbladder, and biliary tract, Radiol. Clin. North Am., Vol. 40, No. 6, 2002.

[36] Sheth, S. and Fishman, E. K., Imaging of uncommon tumors of the pancreas, Radiol. Clin. North Am., Vol. 40, pp. 1273-1287, 2002.

[37] Stanley, R. J. and Semelka, R. C., Pancreas, In: Computed Body Tomography with MRI Correlation, Lee, J. K. T., Sagel, S. S., Stanley, R. J., and Heiken, J. P., eds., Lippincott Raven, pp. 915-936, 1998.

[38] Sheedy, P. F., II, Stephens, D. H., Hattery, R. R., MacCaty, R. L., and Williamson, B., Jr., Computed Tomography of the Pancreas: Whole Body Computed Tomography, Radiol. Clin. North Am., Vol. 15, No. 3, pp. 349366, 1977.

[39] Masero, V., Leon-Rojas, J. M., and Moreno, J., Volume reconstruction for health care: A survey of computational methods, Ann. N Y Acad. Sci., Vol. 980, pp. 198-211, 2000.

[40] Udupa, J. K., Three-dimensional visualization and analysis methodologies: A current perspective, Radiographics, Vol. 19, No. 3, pp. 783-806, 1999.

[41] Gonzalez, R. C. and Woods, R. E., (Eds.), Digital Image Processing, 2nd edn., Computer Science Press, Prentice Hall, NJ, 2002.

[42] Kobashi, M. and Shapiro, L. G., Knowledge-based organ identification from CT images, Patt. Recogn., Vol. 28, No. 4, pp. 475-491, 1995.

[43] Dawant, B. M. and Zijdenbos, A. P., Image segmentation, In: Handbook of Medical Imaging, Volume 2: Medical Image Processing and Analysis, Fitzpatrick, J. M. and Sonka, M., eds., SPIE, pp. 71-127, 2000.

[44] Schiemann, T., Michael, B., Tiede, U., and Hohne, K. H., Interactive 3D-segmentation, SPIE, Vol. 1808, pp. 376-383, 1992.

[45] Ikeda, M., Shigeki, I., Ishigaki, T., and Yamauchi, K., Evaluation of a neural network classifier for pancreatic masses based on CT findings, Comput. Med Imaging Graphics, Vol. 21, No. 3, pp. 175-183, 1997.

[46] Clarke, L. P., Velthuizen, R. P., Camacho, M. A., Heine,J. J.,Vaidyanathan, M., Hall, L. O., Thatcher, R. W., and Silbiger, M. L., Review of MRI segmentation: Methods and applications, Magn. Reson. Imaging, Vol. 13,

[47] Bensaid, A. M., Improved Fuzzy Clustering for Pattern Recognition with Applications to Image Segmentation., Ph.D. Dissertation, Department of Computer Science, University of South Florida, 1994.

[48] Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press, New York, 1981.

[49] Bensaid, A. M., Bezdek, J. C., Hall, L. O., and Clarke, L. P., A partially supervised fuzzy c-means algorithm for segmentation of MR images, SPIE, Vol. 1710, pp. 522-528, 1992.

[50] Bensaid, A. M., Hall, L. O., Bezdek, J. C., Clarke, L. P., Silbiger, M. L., Ar-rington, J. A., and Murtagh, R. F., Validity-guided (re)clustering with application to image segmentation, IEEE Trans. Fuzzy Sys., Vol. 4, No. 2, pp. 112-123, 1996.

[51] Clark, M. C., Hall, L. O., Goldgof, D. B., Clarke, L. P., Velthuizen, R. P., and Silbiger, M. S., MRI segmentation using fuzzy clustering techniques, IEEE Eng. Med. Biol. Magazine, Vol. 13, No. 5, pp. 730-742, 1994.

[52] Clarke, L. P., Velthuizen, R. P., Phuphanich, S., Schellenberg, J. D., Arrington, J. A., and Silbiger, M. L., MRI: Stability of three supervised segmentation techniques, Magn. Reson. Imaging, Vol. 11, No. 1, pp. 95106, 1993.

[53] Vaidyanathan, M., Clarke, L. P., Velthuizen, R. P., Phuphanich, S., Bensaid, A. M., Hall, L. O., Bezdek, J. C., Greenburg, H., Trotti, A., and Silbiger, M., Comparison of supervised MRI segmentation methods for tumor volume determination during therapy, Magn. Reson. Imaging, Vol. 13, No. 5, pp. 719-728, 1995.

[54] Velthuizen, R. P., Clarke, L. P., Phuphanich, S., Hall, L. O., Bensaid, A. M., Arrington, J. A., Greenberg, H. M., and Silbiger, M. L., Unsupervised measurement of brain tumor volume on MR images, J. Magn. Reson. Imaging, Vol. 5, No., 5, pp. 594-605, 1995.

[55] Velthuizen, R. P., Hall, L. O., and Clarke, L. P., An initial investigation of feature extraction with genetic algorithms for fuzzy clustering, Biomed. Eng., Appl., Basis Commun., Vol. 8, No. 6, pp. 496-517, 1996.

[56] Velthuizen, R. P. and Gangadharan, D., Mammographic mass classification: Initial results, In: SPIE Medical Imaging Conference, San Diego, CA, February 12-18, 2000.

[57] Li, L., Zheng, Y., Kallergi, M., and Clark, R. A., Improved method for automatic identification of lung regions on chest radiographs, Acad. Radiol., Vol. 8, pp. 629-638, 2001.

[58] Kallergi, M., Carney, G., and Gaviria, J., Evaluating the performance of detection algorithms in digital mammography, Med. Phy., Vol. 26, No. 2, pp. 267-275, 1999.

[59] Kallergi, M., Clark, R. A., and Clarke, L. P., Medical image databases for CAD applications in digital mammography: Design issues, In: Medical Informatics Europe '97, Pappas, C., Maglaveras, N., and Scherrer, J. R., eds., IOS Press, Amsterdam, pp. 601-605, 1997.

[60] Harrell, F. E., Lee, K. L., and Mark, D. B., Tutorial in biostatistics. Mul-tivariate prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Stat. Med., Vol. 15, pp. 361-387, 1996.

[61] Roe, C. A. and Metz, C. E., Dorfman-Berbaum-Metz method for statistical analysis of multireader, multimodality receiver operating characteristic data: Validation with computer simulation, Acad. Radiol., Vol. 4, pp. 298-303, 1997.

[62] Li, L., Zheng, Y., Kallergi, M., and Clark, R. A., Improved method for automatic identification of lung regions on chest radiographs, Acad. Radiol., Vol. 8, pp. 629-638, 2001.

[63] Pavlidis, T., Algorithms for Graphics and Image Processing, Computer Science Press, Rockville, MD, 1982.

[64] Greenberg, S., Aladjem, M., Kogan, D., and Dimitrov, I., Fingerprint image enhancement using filtering techniques, In: International Conference on Pattern Recognition, Vol. 3, Barcelona, Spain, Sept. 3-8, 2000.

[65] Heine, J. J., Deans, S. R., Cullers, D. K., Stauduhar, R., and Clarke, L. P., Multiresolution statistical analysis of high resolution digital mammo-grams, IEEE Trans. Med. Imaging, Vol. 16, pp. 503-15, 1997.

[66] Weaver, J. B., Xu, Y. S., Healy, D. M., Jr., and Cromwell, L. D., Filtering noise from images with wavelet transforms, Magn. Reson. Med., Vol. 21, No. 2, pp. 288-295, 1991.

[67] Hall, L. O., Bensaid, A. M., Clarke, L. P., Velthuizen, R. P., Silbiger, M. L., and Bezdek, J., A Comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Trans. Neural Networks, Vol. 3, No. 5, pp. 672-682, 1992.

[68] Phillips, W. E., Velthuizen, R. P., Phuphanich, S., Hall, L. O., Clarke, L. P., and Silbiger, M. L., Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme, Magn. Reson. Imaging, Vol. 13, No. 2, pp. 277-290, 1995.

[69] Kallergi, M., Gavrielides, M. A., He, L., Berman, C. G., Kim, J. J., and Clark, R. A., A simulation model of mammographic calcifications based on the ACR BIRADS, Acad. Radiol., Vol. 5, pp. 670-679, 1998.

[70] Kallergi, M., He, L., Gavrielides, M., Heine, J. J., and Clarke, L. P., Resolution effects on the morphology of calcifications in digital mammograms, In: Proceedings of VIII Mediterranean Conference on Medical and

Biological Engineering and Computing, Medicon' 98, Lemesos, Cyprus, (June 14-17, 1998), CD-ROM, ISBN 9963-607-13-6.

[71] Zhang, Y. J., A review of recent evaluation methods for image segmentation, In: Proceedings of International Symposium on Signal Processing and its Applications, Malaysia, August 13-16, 2001.

[72] Zhang, Y. J., A survey on evaluation methods for image segmentation, Patt. Recogn., Vol. 29, No. 8, pp. 1335-1346, 1996.

[73] Gerig, G., Jomier, M., and Chakos, M., Valmet: A new validation tool for assessing and improving 3D object segmentation, MICCAI, Vol. 2208, pp. 516-528, 2001.

[74] Chalana, V. and Kim, Y., A methodology for evaluation of boundary detection algorithms on medical images, IEEE Trans. Med. Imaging, Vol. 16, No. 5, pp. 642-652, 1997.

[75] 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.

[76] Motulsky, H., Intuitive Biostatistics, Oxford University Press, USA, 1995.

[77] Mould, R. F., Introductory Medical Statistics, 3rd edn., Institute of Physics Publishing, Bristol, 1998.

[78] Metz, C. E., ROC methodology in radiologic imaging, Invest. Radiol., Vol. 21, pp. 720-733, 1986.

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