3D Multi-parametric Breast MRI Segmentation Using Hierarchical Support Vector Machine with Coil Sensitivity Correction

Academic Radiology

Volumn 20, Issue 2, 2013, Pages 137-147

  Wang, Yi ^a   Morrell, Glen ^a   Heibrun, Marta E ^a   Payne, Allison ^a   Parker, Dennis L ^a  

^a UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

Author keywords

Breast MRI segmentation; Coil sensitivity correction; Multiparametric input; Support vector machine

Indexed keywords

ARTICLE; BODY DENSITY; BREAST; BREAST CANCER; BREAST FIBROADENOMA; BREAST LESION; CANCER DIAGNOSIS; CANCER RISK; CANCER STAGING; CASE REPORT; CLASSIFICATION ALGORITHM; CLINICAL CLASSIFICATION; CLINICAL EFFECTIVENESS; COMPARATIVE STUDY; CONTROLLED STUDY; DIAGNOSTIC TEST ACCURACY STUDY; DIFFUSION WEIGHTED IMAGING; HUMAN; IMAGE RECONSTRUCTION; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; ULTRASOUND THERAPY; VOLUNTEER;

ALGORITHMS; ARTIFACTS; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES;

EID: 84873523156     PISSN: 10766332     EISSN: 18784046     Source Type: Journal    
DOI: 10.1016/j.acra.2012.08.016     Document Type: Article

References (34)

1. Sinha S., Sinha U. Recent advances in breast MRI and MRS. NMR Biomed 2009, 22:3-16.
2. Weatherall P.T., Evans G.F., Metzger G.J., et al. MRI vs. histologic measurement of breast cancer following chemotherapy: comparison with x-ray mammography and palpation. J Magn Reson Imaging 2001, 13:868-875.
3. Lee N.A., Rusinek H., Weinreb J., et al. Fatty and fibroglandular tissue volumes in the breasts of women 20-83 years old: comparison of X-ray mammography and computer-assisted MR imaging. AJR Am J Roentgenol 1997, 168:501-506.
4. Bhooshan N., Giger M.L., Jansen S.A., et al. Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology 2010, 254:680-690.
5. Ertas G., Gulcur H.O., Osman O., et al. Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Comput Biol Med 2008, 38:116-126.
6. Ertas G., Gulcur H.O., Tunaci M. Improved lesion detection in MR mammography: three-dimensional segmentation, moving voxel sampling, and normalized maximum intensity-time ratio entropy. Acad Radiol 2007, 14:151-161.
7. Gilhuijs K.G., Giger M.L., Bick U. Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys 1998, 25:1647-1654.
8. Nie K., Chen J.H., Yu H.J., et al. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 2008, 15:1513-1525.
9. Nie K., Chen J.H., Chan S., et al. Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys 2008, 35:5253-5262.
10. Lin M., Chan S., Chen J.H., et al. A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI. Med Phys 2011, 38:5-14.
11. Kuroda K., Oshio K., Mulkern R.V., et al. Optimization of chemical shift selective suppression of fat. Magn Reson Med 1998, 40:505-510.
12. Jacobs M.A., Barker P.B., Bluemke D.A., et al. Benign and malignant breast lesions: diagnosis with multiparametric MR imaging. Radiology 2003, 229:225-232.
13. Wang CM, Mai XX, Lin GC, et al. Classification for breast MRI using support vector machine. Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology 2008:362-367.
14. Heywang S.H., Bassermann R., Fenzl G., et al. MRI of the breast-histopathologic correlation. Eur J Radiol 1987, 7:175-182.
15. Meyer C.R., Bland P.H., Pipe J. Retrospective correction of intensity inhomogeneities in MRI. IEEE Trans Med Imaging 1995, 14:36-41.
16. Guillemaud R., Brady M. Estimating the bias field of MR images. IEEE Trans Med Imaging 1997, 16:238-251.
17. Brey W.W., Narayana P.A. Correction for intensity falloff in surface coil magnetic resonance imaging. Med Phys 1988, 15:241-245.
18. McVeigh E.R., Bronskill M.J., Henkelman R.M. Phase and sensitivity of receiver coils in magnetic resonance imaging. Med Phys 1986, 13:806-814.
19. Bezdek J.C. Pattern recognition with fuzzy objective function algorithms 1981, Plenum Press, New York.
20. Chen W., Giger M.L., Bick U. A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol 2006, 13:63-72.
21. Barck K.H., Willis B., Ross J., et al. Viable tumor tissue detection in murine metastatic breast cancer by whole-body MRI and multispectral analysis. Magn Reson Med 2009, 62:1423-1430.
22. Yang S.C., Wang C.M., Hsu H.H., et al. Contrast enhancement and tissues classification of breast MRI using Kalman filter-based linear mixing method. Comput Med Imaging Graph 2009, 33:187-196.
23. Chung P.C., Wang C.M., Yang S.H., et al. Tissues classification for breast MRI contrast enhancement using spectral signature detection approach. IEEE International Conference on Systems, Man and Cybernetics 2006, 5:3917-3921.
24. Du Y.P., Parker D.L., Davis W.L., et al. Reduction of partial-volume artifacts with zero-filled interpolation in three-dimensional MR angiography. J Magn Reson Imaging 1994, 4:733-741.
25. Glover G.H., Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 1991, 18:371-383.
26. Bernstein M.A., King K.F., Zhou Z.J. Handbook of MRI pulse sequences 2004, Academic Press, Boston.
27. Moon-Ho Song S., Napel S., Pelc N.J., et al. Phase unwrapping of MR phase images using Poisson equation. IEEE Trans Image Process 1995, 4:667-676.
28. Nie K., Chang D., Chen J.H., et al. Impact of skin removal on quantitative measurement of breast density using MRI. Med Phys 2010, 37:227-233.
29. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986, 8:679-698.
30. Vemuri P., Kholmovski E.G., Parker D.L., et al. Coil sensitivity estimation for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging. Inf Process Med Imaging 2005, 19:603-614.
31. Roemer P.B., Edelstein W.A., Hayes C.E., et al. The NMR phased array. Magn Reson Med 1990, 16:192-225.
32. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20:273-297.
33. Chang C.C., Lin C.J. LIBSVM: a library for support vector machine. ACM Trans Intell Syst Technol 2011, 2:1-27.
34. Casasent D., Wang Y.C. A hierarchical classifier using new support vector machines for automatic target recognition. Neural Netw 2005, 18:541-548.