Model-based region-of-interest selection in dynamic breast MRI

Journal of Computer Assisted Tomography

Volumn 30, Issue 4, 2006, Pages 675-687

  Forbes, Florence ^a   Peyrard, Nathalie ^b   Fraley, Chris ^c   Georgian Smith, Dianne ^d   Goldhaber, David M ^e   Raftery, Adrian E ^c  

^a INRIA RHÔNE ALPES   (France)

^b CEMAGREF   (France)

^c UNIVERSITY OF WASHINGTON   (United States)

^d MASSACHUSETTS GENERAL HOSPITAL   (United States)

^e International MRI Accreditation Resources   (United States)

Author keywords

Bayesian morphology; Dynamic contrast enhanced breast MRI; Image segmentation; Magnetic resonance imaging; Model based clustering; MR mammography; Region of interest selection; Time signal intensity curves

Indexed keywords

IMAGE ENHANCEMENT; IMAGE SEGMENTATION; MAMMOGRAPHY; SIGNAL ANALYSIS;

BAYESIAN; BAYESIAN MORPHOLOGY; BREAST MAGNETIC-RESONANCE IMAGING; DYNAMIC CONTRAST-ENHANCED; DYNAMIC CONTRAST-ENHANCED BREAST MAGNETIC RESONANCE IMAGING; IMAGES SEGMENTATIONS; MODEL-BASED CLUSTERING; MR MAMMOGRAPHY; REGIONS OF INTEREST SELECTIONS; SIGNAL-INTENSITY CURVES; TIME SIGNALS; TIME-SIGNAL INTENSITY CURVE;

MAGNETIC RESONANCE IMAGING;

ARTICLE; BAYES THEOREM; BREAST; BREAST EXAMINATION; BREAST LESION; CLINICAL ARTICLE; DATA BASE; DIAGNOSTIC VALUE; HUMAN; IMAGE ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL;

EID: 33746218005     PISSN: 03638715     EISSN: None     Source Type: Journal    
DOI: 10.1097/00004728-200607000-00020     Document Type: Article

References (47)

1. Padhani AR. Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magnet Resonance Imag. September 25, 2002;16:407-422.
2. Kneeshaw PJ, Turnbull LW, Drew PJ. Current applications and future directions of MR mammography. Brit J Canc. 2003;88:4-10.
3. Szabó BK, Aspelin P, Kristofferson Wilberg M, et al. Dynamic MR imaging of the breast. Acta Radiologica. July 2003;44:379.
4. Bluemke DA, Gatsonis CA, Chen MH, et al. Magnetic resonance imaging of the breast prior to biopsy. J Am Med Assoc. December 8, 2004;292:2735-2742.
5. Hylton N. Magnetic resonance imaging of the breast: opportunities to improve breast cancer management. J Clin Oncol. March 10, 2005;23:1678-1684.
6. Liberman L, Menell JH. Breast image reporting and data system (BI-RADS). Radiol Clin North Amer. May 2002;40:409-430.
7. Chen W, Giger ML, Lan L, et al. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys. May 2004;31:1076-1082.
8. Sardanelli F. MultiHance for dynamic MR imaging of the breast. Eur Radiol Suppl. June 2004;14:65-70.
9. Deurloo EE, Muller SH, Peterse JL, et al. Clinically and mammographically occult breast lesions on MR images: potential effect of computerized assessment on clinical reading. Radiology. 2005;234:693-701.
10. Pediconi F, Catalano C, Venditti F, et al. Color-coded automated signal intensity curves for detection and characterization of breast lesions: preliminary evaluation of a new software package for integrated magnetic resonance-based breast imaging. Invest Radiol. July 2005;40:448-457.
11. Pfleiderer SO, Marx C, Vagner J, et al. Magnetic resonance-guided large-core breast biopsy inside a 1.5-T magnetic resonance scanner using an automatic system: in vitro experiments and preliminary clinical experience in four patients. Invest Radiol. July 2005;40:458-464.
12. Sampat MP, Markey MK, Bovik AC. Computer-aided detection and diagnosis in mammography. In: Bovik A, ed. Handbook of Image and Video Processing, 2nd ed. Academic Press; 2005:1195-1217.
13. Wells W, Grimson W. Adaptive segmentation of MRI data. IEEE Trans Med Imag. 1996;15:429-442.
14. Held K, Kops E, Krause B, et al. Markov random field segmentation of brain MR images. IEEE Trans Med Imag. 1997;16:878-886.
15. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the Expectation-Maximization algorithm. IEEE Trans Med Imag. 2001;20:45-57.
16. Li AJ. Streaming Data Analysis Algorithms and Implementations. Technical report, TAMI RIL report; 1999.
17. Schwarz G. Estimating the dimension of a model. Ann Statist. 1978;6:461-464.
18. Kass RE, Raftery AE. Bayes factors. J Amer Statist Assoc. 1995;90:733-795.
19. McLachlan GJ, Peel D. Finite Mixture Models. Wiley; 2000.
20. Fraley C, Raftery AE. Model-based clustering, discriminant analysis and density estimation. J Amer Statist Assoc. 2002;97:611-631.
21. Forbes F, Raftery AE. Bayesian morphology: fast unsupervised Bayesian image analysis. J Amer Statist Assoc. 1999;94:555-568.
22. Dempster AP, Laird N, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J R Statist Soc, Series B. 1977;39:1-38.
23. Fraley C, Raftery AE. Enhanced software for model-based clustering, density estimation, and discriminant analysis: MCLUST. J Classific. 2003;20:263-286.
24. Banfield JD, Raftery AE. Model-based Gaussian and non-Gaussian clustering. Biometrics. 1993;49:803-821.
25. Heijmans HJ. Mathematical morphology: a modern approach in image processing based on algebra and geometry. SIAM Rev. 1995;37:1-36.
26. Besag JE. On the statistical analysis of dirty pictures. JR Statist Soc, Series B. 1986;48:259-302.
27. Kuhl CK. MRI of breast tumors. Eur Radiol. January 2000;10:46-58.
28. Helbich TH. Contrast-enhanced magnetic resonance imaging of the breast. Eur J Radiol. 2000;34:208-219.
29. Armitage PA, Behrenbruch CP, Brady JM, et al. Extracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast. Med Image Anal. August 2005;9:315-329.
30. Preda A, Turefschek K, Daldrup H, et al. The choice of region of interest measures in contrast-enhanced magnetic resonance image characterization of experimental breast tumors. Invest Radiol. June 2005;40:349-354.
31. Sinha S, Lucas-Quesada FA, DeBruhl ND, et al. Multifeature analysis of Gd-enhanced MR images of breast lesions. J Magnet Reson Imag. 1997;7:1016-1026.
32. Fischer H, Otte M, Ehritt-Braun C, et al. Local elastic matching and pattern recognition in MR mammography. Int J Imag Syst Technol. March 3, 1999;10:199-206.
33. Penn AI, Bolinger L, Schnall MD, et al. Discrimination of MR images of breast masses with fractal-interpolation function models. Acad Radiol. 1999;6:156-163.
34. Nunes LW, Schnall MD, Orel SG. Update of breast MR imaging architectural interpretation model. Radiology. 2001;219:484-494.
35. de Pasquale F, Barone P, Sebastiani G, et al. Bayesian analysis of dynamic breast images. J R Statist Soc, Series C - Appl Statist. August 2004;53:475-493.
36. Tzacheva AA, Najaran K, Brockway JP. Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks. Med Phys. February 19, 2003;17:337-342.
37. Vornweg TW, Buscema M, Kauczor HU, et al. Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography. Med Phys. 2003;30:2350-2359.
38. Lucht RE, Delorme S, Hei J, et al. Classification of signal-time curves obtained by dynamic magnetic resonance mammography: statistical comparison of quantitative methods. Invest Radiol. July 2005;40:442-447.
39. Yoo S-S, Gil Choi B, Han J-Y, et al. Independent component analysis for the examination of dynamic contrast-enhanced breast magnetic resonance data: preliminary study. Invest Radiol. December 2002;37:647-654.
40. Liu PF, Debatin JF, Cadaff RF, et al. Improved diagnostic accuracy in dynamic contrast-enhanced MRI of the breast by combined quantitative and qualitative analysis. Brit J Radiol. 1998;71:501-509.
41. Tozaki M, Igarashi T, Matsushima S, et al. High-spatial-resolution MR imaging of focal breast masses: interpretation model based on kinetic and morphological parameters. Rad Med. 2005;23:43-50.
42. Wiener JI, Schilling KJ, Adami C, et al. Assessment of suspected breast cancer by MRI: a prospective clinical trial using a combined kinetic and morphologic analysis. Amer J Roentgenol. 2005;184:878-886.
43. Jacobs MA, Barker PB, Argani P, et al. Combined dynamic contrast breast MR and proton spectroscopic imaging: a feasibility study. J Magnet Resonance Imag. December 20, 2004;21:23-28.
44. Vornweg TW, Teifke A, Kunz RP, et al. Combination of low and high resolution sequences in two orientations for dynamic contrast-enhanced MRI of the breast: more than a compromise. Eur Radiol. October 2004;14:1732-1742.
45. Choi B, Han B-K, Choe YH, et al. Three-phase dynamic breast magnetic imaging with two-way subtraction. J Comput Aided Tomogr. 2005;29:834-841.
46. Miyake K, Hayakawa K, Nishino M, et al. Benigh or malignant? differentiating breast lesions with computed tomography attenuation values on dynamic computed tomography mammography. J Comput Aided Tomogr. 2005;29:772-779.
47. Woodhams R, Matsunaga K, Iwabuchi K, et al. Diffusion-weighted imaging of malignant breast tumors: the usefulness of apparent diffusion coefficient (ADC) value and ADC map for the detection of malignant breast tumors and evaluation of cancer extension. J Compu Aided Tomogr. 2005;29:644-649.