Breast Mass Segmentation in Mammography Using Plane Fitting and Dynamic Programming

Academic Radiology

Volumn 16, Issue 7, 2009, Pages 826-835

  Song, Enmin ^a   Jiang, Luan ^a   Jin, Renchao ^a   Zhang, Lin ^a   Yuan, Yuan ^a   Li, Qiang ^a  

^a South Central University for Nationalities 708 Minyuan Road   (China)

Author keywords

Breast mass segmentation; computer aided diagnosis; dynamic programming; mammography; plane fitting

Indexed keywords

ARTICLE; BREAST TUMOR; CANCER CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; CONTROLLED STUDY; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; FEMALE; HUMAN; IMAGE ANALYSIS; IMAGING SYSTEM; MAJOR CLINICAL STUDY; MAMMOGRAPHY; PRIORITY JOURNAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAMMOGRAPHY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 66649094498     PISSN: 10766332     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.acra.2008.11.014     Document Type: Article

References (32)

1. Jemal A.J., Siegel R., Ward E., et al. Cancer statistics, 2007. CA Cancer J Clin 57 (2007) 43-66
2. Feig S.A., D'Orsi C.J., Hendrick R.E., et al. American College of Radiology guidelines for breast cancer screening. AJR Am J Roentgenol 171 (1998) 29-33
3. Cady B., and Michaelson J.S. The life-sparing potential of mammographic screening. Cancer 91 (2001) 1699-1703
4. Bird R.E., Wallace T.W., and Yankaskas B.C. Analysis of cancers missed at screening mammography. Radiology 184 (1992) 613-617
5. Doi K., MacMahon H., and Katsuragawa S. Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol l31 (1999) 97-109
6. Huo Z., Giger M.L., Vyborny C.J., et al. Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness. Acad Radiol 7 (2000) 1077-1084
7. Sahiner B., Chan H.P., Petrick N., et al. Improvement of mammographic mass characterization using spiculation measures and morphological features. Med Phys 28 (2001) 1455-1465
8. Cheng H.D., Shi X.J., Min R., et al. Approaches for automated detection and classification of masses in mammograms. Pattern Recognition 39 (2006) 646-668
9. Sahiner B., Petrick N., Chan H.P., et al. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization. IEEE Trans Med Imaging 20 (2001) 1275-1284
10. Petrick N., Chan H.P., Wei D., et al. Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. Med Phys 23 (1996) 1685-1696
11. Zheng B., Chang Y.H., and Gur D. Computer detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis. Acad Radiol 2 (1995) 959-966
12. Comer M.L., Liu S., and Delp E.J. Statistical segmentation of mammograms. Presented at: Digital Mammography. In: Doi K. (Ed). International Congress Series (1996), Elsevier, Amsterdam, The Netherlands 471-474
13. Catarious D.M., Baydush A.H., and Floyd C.E. Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system. Med Phys 31 (2004) 1512-1520
14. Yuan Y., Giger M.L., Li H., et al. A dual-stage method for lesion segmentation on digital mammograms. Med Phys 34 (2007) 4180-4193
15. Amini A.A., Weymouth T.E., and Jain R.C. Using dynamic programming for solving variational problem in vision. IEEE Trans Pattern Anal Mach Intell 12 (1990) 855-867
16. Yamada H., Merritt C., and Kasvand T. Recognition of kidney glomerulus by dynamic programming matching method. IEEE Trans Pattern Anal Mach Intell 10 (1988) 731-737
17. Nuyts J., Suetens P., Oosterlinck A., et al. Delineation of ECT images using global constraints and dynamic programming. IEEE Trans Med Imaging 10 (1991) 489-498
18. Aoyama M., Li Q., Katsuragawa S., et al. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Med Phys 30 (2003) 387-394
19. Timp S., and Karssemeijer N. A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med Phys 31 (2004) 958-971
20. Domínguez A.R., and Nandi A.K. Improved dynamic-programming-based algorithms for segmentation of masses in mammograms. Med Phys 34 (2007) 4256-4269
21. Wang J., Engelmann R., and Li Q. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. Med Phys 34 (2007) 4678-4689
22. Fracheboud J., deKoning H.J., Beemsterboer P.M.M., et al. Nation-wide breast cancer screening in The Netherlands: results of initial and subsequent screening 1990-1995. Int J Cancer 75 (1998) 694-698
23. Suckling J., Parker J., Dance D., et al. The Mammographic Image Analysis Society digital mammogram database. Presented at: Exerpta Medica. International Congress Series 1069 (1994) 375-378. http://peipa.essex.ac.uk/info/mias.html Available at:http://peipa.essex.ac.uk/info/mias.html
24. Heath M., Bowyer K.W., Kopans D., et al. Current status of the digital database for screening mammography. In: Karssemeijer N., Thijssen M., and Hendriks J. (Eds). Presented at: Digital Mammograpy (1998), Kluwer, Dordrecht, Boston, MA 457-460. http://marathon.csee.usf.edu/ Available at: http://marathon.csee.usf.edu/
25. Shi J., and Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22 (2000) 888-905
26. Heath M, Bowyer KW, Kopans D. The Digital Database for Screening Mammography. Presented at: 5th International Workshop on Digital Mammography. Toronto, Canada. June 2000.
27. American College of Radiology Breast Imaging Reporting and Data System Atlas (BI-RADS Atlas). 4th ed. (2003), American College of Radiology, Reston, VA
28. Farid H. Blind inverse γ correction. IEEE Trans Image Proc 10 (2001) 1428-1433
29. Image Segmentation with Normalized Cuts. Available at:. http://www.cis.upenn.edu/∼jshi/software/ Accessed November 30, 2006
30. Kupinski M.A., and Giger M.L. Automated seeded lesion segmentation on digital mammograms. IEEE Trans Med Imaging 17 (1998) 510-517
31. Te Brake G.M., and Karssemeijer N. Segmentation of suspicious densities in digital mammograms. Med Phys 28 (2001) 259-266
32. Hammoude A. An empirical parameter selection method for endocardial border identification algorithms. Comput Med Imaging Graph 25 (2001) 33-45