The use of a priori information in the detection of mammographic microcalcifications to improve their classification
2003; Wiley; Volume: 30; Issue: 5 Linguagem: Inglês
10.1118/1.1559884
ISSN2473-4209
AutoresMarı́a F. Salfity, Robert M. Nishikawa, Yulei Jiang, John Papaioannou,
Tópico(s)Gene expression and cancer classification
ResumoMedical PhysicsVolume 30, Issue 5 p. 823-831 Radiation imaging physics The use of a priori information in the detection of mammographic microcalcifications to improve their classification Marı́a F. Salfity, Marı́a F. Salfity Instituto de Fı́sica Rosario, CONICET-UNR, Bv. 27 de Febrero 210 bis, 2000 Rosario, ArgentinaSearch for more papers by this authorRobert M. Nishikawa, Robert M. Nishikawa Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637Search for more papers by this authorYulei Jiang, Yulei Jiang Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637Search for more papers by this authorJohn Papaioannou, John Papaioannou Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637Search for more papers by this author Marı́a F. Salfity, Marı́a F. Salfity Instituto de Fı́sica Rosario, CONICET-UNR, Bv. 27 de Febrero 210 bis, 2000 Rosario, ArgentinaSearch for more papers by this authorRobert M. Nishikawa, Robert M. Nishikawa Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637Search for more papers by this authorYulei Jiang, Yulei Jiang Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637Search for more papers by this authorJohn Papaioannou, John Papaioannou Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637Search for more papers by this author First published: 22 April 2003 https://doi.org/10.1118/1.1559884Citations: 17AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract In this work, we present a calcification-detection scheme that automatically localizes calcifications in a previously detected cluster in order to generate the input for a cluster-classification scheme developed in the past. The calcification-detection scheme makes use of three pieces of a priori information: the location of the center of the cluster, the size of the cluster, and the approximate number of calcifications in the cluster. This information can be obtained either automatically from a cluster-detection scheme or manually by a radiologist. It is used to analyze only the portion of the mammogram that contains a cluster and to identify the individual calcifications more accurately, after enhancing them by means of a "Difference of Gaussians" filter. Classification performances (patient-based cluster-based comparable to those obtained by using manually- identified calcifications (patient-based cluster-based can be achieved. REFERENCES 1S. A. Feig, "Decreased breast cancer mortality through mammographic screening: Results of clinical trials," Radiology 167, 659–665 (1988). 0033-8419 2C. R. Smart, R. E. Hendrick, J. H. Rutledge, and R. A. Smith, "Benefit of mammography screening in women ages 40 to 49 years: Current evidence from randomized controlled trials," Cancer 5, 1619–1626 (1995).0008-543X 3CancerNet 2000, a service of the National Cancer Institute, http://www.cancernet.nci.nih.gov. 4E. A. Sickles, "Mammographic features of 300 consecutive nonpalpable breast cancers," Am. Q. Roentgenol. 146, 661–663 (1986). 0099-5401 5A. M. Knutzen and J. J. Gisvold, "Likelihood of malignant disease for various categories of mammographicaly detected, nonpalpable breast lesions," Mayo Clin. Proc. 68, 454–460 (1993). 0025-6196 6K. Doi, M. L. Giger, R. M. Nishikawa, K. R. Hoffmann, H. MacMahon, R. A. Schmidt, and K. G. Chua, "Digital radiography. A useful clinical tool for computer-aided diagnoses by quantitative analysis of radiographic images," Acta Radiol. 34, 426–439 (1993). 0284-1851 7Y. Wu, M. L. Giger, K. Doi, C. J. Vyborny, R. A. Schmidt, and C. E. Metz, "Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer," Radiology 187, 81–87 (1993). 0033-8419 8Y. Jiang, R. M. Nishikawa, D. E. Wolverton, C. E. Metz, M. L. Giger, R. A. Schmidt, C. J. Vyborny, and K. Doi, "Malignant and benign clustered microcalcifications: automated feature analysis and classification," Radiology 198, 671–678 (1996). 0033-8419 9J. A. Baker, P. J. Kornguth, J. Y. Lo, and C. E. J. Floyd, "Artificial neural network: improving the quality of breast biopsy recommendations," Radiology 198, 131–135 (1996). 0033-8419 10H.-P. Chan, B. Sahiner, N. Petrick, M. A. Helvie, K. L. Lam, D. D. Adler, and M. M. Goodsitt, "Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network," Phys. Med. Biol. 42, 549–567 (1997). 0031-9155 11D. J. Getty, R. M. Pickett, C. J. D'Orsi, and J. A. Swets, "Enhanced interpretation of diagnostic images," Invest. Radiol. 23, 240–252 (1988). 0020-9996 12Y. Jiang, R. M. Nishikawa, R. A. Schimdt, C. E. Metz, M. L. Giger, and K. Doi, "Improving breast cancer diagnosis with computer-aided detection," Acad. Radiol. 6, 22–33 (1999).1076-6332 13H.-P. Chan, B. Sahiner, M. A. Helvie, N. Petrick, M. A. Roubidoux, T. E. Wilson, D. D. Adler, C. Paramagul, J. S. Newman, and S. Sanjay-Gopal, "Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study," Radiology 212, 817–827 (1999). 0033-8419 14H.-P. Chan, K. Doi, C. J. Vyborny, H. MacMahon, and P. M. Jokich, "Image feature analysis and computer-aided diagnosis in digital radiography. 1. Automated detection of microcalcifications in mammography," Med. Phys. 14, 538–548 (1987). 0094-2405 15H.-P. Chan, K. Doi, C. J. Vyborny, R. A. Schmidt, C. E. Metz, K. L. Lam, T. Ogura, Y. Wu, and H. MacMahon, "Improvement in radiologists' detection of clustered microcalcifications on mammograms: The potential of computer-aided diagnosis," Invest. Radiol. 25, 1102–1110 (1990). 0020-9996 16R. M. Nishikawa, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, "Computer-aided detection of clustered microcalcifications: An improved method for grouping detected signals," Med. Phys. 20, 1660–1666 (1993). 0094-2405 17R. M. Nishikawa, Y. Jiang, M. L. Giger, R. A. Schmidt, C. J. Vyborny, W. Zhang, J. Papaioannou, U. Bick, R. Nagel, and K. Doi, " Performance of automated CAD schemes for the detection and classification of clustered microcalcifications," in Digital Mammography, edited by A. G. Gale, S. M. Astley, D. R. Dance, and A. Y. Carins (Elsevier, Amsterdam, Holland, 1994), pp. 13–20. 18W. Zhang, K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt, "An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms," Med. Phys. 23, 595–601 (1996). 0094-2405 19R. H. Nagel, R. M. Nishikawa, J. Papaioannou, and K. Doi, "Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms," Med. Phys. 25, 1502–1506 (1998). 0094-2405 20R. M. Nishikawa, M. L. Giger, D. E. Wolverton, R. A. Schmidt, and K. Doi, " Prospective testing of a clinical CAD workstation for the detection of breast lesions on mammograms," in Computer-Aided Diagnosis in Medical Imaging, edited by K. Doi, H. MacMahon, M. L. Giger and K. R. Hoffmann (Elsevier, Amsterdam, Holland, 1998), pp. 209–214. 21Y. Jiang, R. M. Nishikawa, and J. Papaioannou, "Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications," Med. Phys. 28, 1949–1957 (2001). 0094-2405 22American College of Radiology, Breast Imaging Reporting and Data System (American College of Radiology, Reston VA, 1998). 23J. C. Russ, The Image Processing Handbook (CRC, Boca Raton, 1992). 24C. E. Metz, "ROC methodology in radiologic imaging," Invest. Radiol. 21, 720–733 (1986). 0020-9996 25C. E. Metz, "Some practical issues of experimental design and data analysis in radiological ROC studies," Invest. Radiol. 24, 234–245 (1989). 0020-9996 26Y. Jiang, C. E. Metz, and R. M. Nishikawa, "A receiver operating characteristic partial area index for highly sensitive diagnostic tests," Radiology 201, 745–750 (1996). 0033-8419 27D. C. Edwards, M. A. Kupinski, R. Nagel, R. M. Nishikawa, and J. Papaioannou, " Using a Bayesian Neural Network to optimally eliminate false-positive microcalcification detections in a CAD scheme," in Digital Mammography, edited by M. J. Yaffe (Medical Physics Publishing, Toronto, Canada, 2001), pp. 168–173. 28Z. Huo and M. L. Giger, "Evaluation of a computer segmentation method based on performances of an automated classification method," Proc. SPIE 3981, 16–21 (2000). 0277-786X Citing Literature Volume30, Issue5May 2003Pages 823-831 ReferencesRelatedInformation
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