Academic Radiology
Volume 13, Issue 1 , Pages 63-72 , January 2006

A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1

Received 25 July 2005 ,Revised 25 August 2005 ,Accepted 27 August 2005.

References 

  1. Schnall MD . Breast MR imaging . Radiol Clin North Am . 2003;41:43–50
  2. Heywang SH , Hahn D , Schmidt H , et al.   MR imaging of the breast using gadolinium-DTPA . J Comput Assist Tomogr . 1986;10:199–204
  3. Heywang SH , Wolf A , Pruss E , Hilbertz T , Eiermann W , Permanetter W . MR imaging of the breast with Gd-DTPA (use and limitations) . Radiology . 1989;171:95–103
  4. Kaiser WA , Zeitler E . MR imaging of the breast (fast imaging sequence with and without Gd-DTPA) . Radiology . 1989;170:681–686
  5. Morris EA . Breast cancer imaging with MRI . Radiol Clin North Am . 2002;40:443–466
  6. Gilhuijs KGA , Giger ML , Bick U . Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging . Med Phys . 1998;25:1647–1654
  7. Chen W , Giger ML , Lan L , Bick U . Computerized interpretation of breast MRI (investigation of enhancement-variance dynamics) . Med Phys . 2004;31:1076–1082
  8. Boetes C , Mus RD , Holland R , et al.   Breast tumors (comparative accuracy of MR imaging relative to mammography and US for demonstrating extent) . Radiology . 1995;197:743–747
  9. Davis PL , Staiger MJ , Harris KB , Ganott MA , Klementaviciene J , McCarty KS . Breast cancer measurements with magnetic resonance imaging, ultrasonography, and mammography . Breast Cancer Res Treat . 1996;37:1–9
  10. Wiberg MK , Aspelin P , Sylvan M , Bonë B . Comparison of lesion size estimated by dynamic MR imaging, mammography and histopathology in breast neoplasms . Eur Radiol . 2003;13:1207–1212
  11. Kupinski MA , Giger ML . Automated seeded lesion segmentation on digital mammograms . IEEE Trans Med Imaging . 1998;17:510–517
  12. Catarious DM , Baydush AH , Floyd CE . Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system . Med Phys . 2004;31:1512–1520
  13. Horsch K , Giger ML , Venta LA , Vyborny CJ . Automatic segmentation of breast lesions on ultrasound . Med Phys . 2001;28:1652–1659
  14. Madabhushi A , Metaxas DN . Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions . IEEE Trans Med Imaging . 2003;22:155–169
  15. Lucas-Quesada FA , Sinha U , Sinha S . Segmentation strategies for breast tumors from dynamic MR images . J Magn Reson Imaging . 1996;6:753–763
  16. Gihuijs KGA , Giger ML , Bick U . A method for computerized assessment of tumor extent in contrast-enhanced MR images of the breast . In: Computer-Aided Diagnosis in Medical Imaging . Philadelphia, PA: Elsevier; 1999;p. 305–310
  17. Bezdek JC . Pattern Recognition With Fuzzy Objective Function Algorithm . New York: Plenum; 1981;
  18. Bezdek JC , Pal SK . Fuzzy Models for Pattern Recognition . NY: IEEE Press; 1992;
  19. Bezdek JC , Hall LO , Clarke LP . Review of MR image segmentation techniques using pattern recognition . Med Phys . 1993;20:1033–1048
  20. Pham DL , Prince JL . Adaptive fuzzy segmentation of magnetic resonance images . IEEE Trans Med Imaging . 1999;18:737–752
  21. Ahmed MN , Yamany SM , Mohamed N , Farag AA , Moriarty T . A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data . IEEE Trans Med Imaging . 2002;21:193–199
  22. Baumgartner R , Windischberger C , Moser E . Quantification in functional magnetic resonance imaging (fuzzy clustering vs) . correlation analysis. Magn Reson Imaging . 1998;16:115–125
  23. Jahanian H , Hossein-Zadeh G , Soltanian-Zadeh H , Ardekani BA . Controlling the false positive rate in fuzzy clustering using randomization (application to fMRI activation detection) . Magn Reson Imaging . 2004;22:631–638
  24. Haralick RM , Shapiro LG . Computer and Robot Vision . Reading, MA (Addison-Wesley) . 1992;1:28–48
  25. Soille P . In: Morphological Image Analysis (Principles and Applications) . New York: Springer-Verlag; 1999;p. 173–174
  26. Kuhl CK , Mielcareck P , Klaschik S , et al.   Dynamic breast MR imaging (are signal intensity time course data useful for differential diagnosis of enhancing lesion?) . Radiology . 1999;211:101–110
  27. Hoffmann U , Brix G , Knopp MV , Heb T , Lorenz WJ . Pharmacokinetic mapping of the breast (a new method for dynamic MR mammography) . Magn Reson Med . 1995;33:506–514
  28. Condon BR , Patterson J , Wyper D . Image nonuniformity in magnetic resonance imaging (its magnitude and methods for its correction) . Br J Radiol . 1987;60:83–87
  29. Wells WM , Grimson WEL , Kikinis R , Jolesz FA . Adaptive segmentation of MRI data . IEEE Trans Med Imaging . 1996;15:429–442
  30. Guillemaud R , Brady M . Estimating the bias field of MR images . IEEE Trans Med Imaging . 1997;16:238–251
  31. Hayton P . Analysis of Contrast-Enhanced Breast MRI . PhD dissertation . Oxford, UK: University of Oxford; 1998;
  32. Penn AI , Thompson SF , Schnall MD , Loew MH , Bolinger L . Fractal discrimination of MRI breast masses using multiple segmentations . Proc SPIE Med Imaging 2000 (Image Processing) . 2000;3979:959–966

 Supported in part by Breast Cancer Research Program grant no. DAMD17-03-1-0245 from the Department of Defense and grant no. CA89452 from the US Public Health Service. M.L.G. is a shareholder in R2 Technology, Sunnyvale, CA. It is the policy of the University of Chicago that investigators disclose publicly actual or potential significant financial interests that may appear to be affected by the research activities.

PII: S1076-6332(05)00803-2

doi: 10.1016/j.acra.2005.08.035

Academic Radiology
Volume 13, Issue 1 , Pages 63-72 , January 2006

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