Computer aided analysis of breast MRI enhancement kinetics using mean shift c lustering and multifeature iterative region of interest selection

Journal of Magnetic Resonance Imaging

Volumn 36, Issue 5, 2012, Pages 1104-1112

  Stoutjesdijk, Mark J ^a   Zijp, Miranda ^b   Boetes, Carla ^b,c   Karssemeijer, Nico ^b   Barentsz, Jelle O ^b   Huisman, Henkjan ^b  

^a IKAZIA HOSPITAL   (Netherlands)

^b RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^c MAASTRICHT UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

breast imaging; clustering; computer aided diagnosis; dynamic contrast enhanced MRI; mean shift; multi feature iterative ROI selection

Indexed keywords

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BREAST LESION; CLUSTER ANALYSIS; COMPUTER ANALYSIS; CONTRAST ENHANCEMENT; DISEASE CLASSIFICATION; FEMALE; HUMAN; MAJOR CLINICAL STUDY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; RADIOLOGICAL PARAMETERS; RADIOLOGIST; RECEIVER OPERATING CHARACTERISTIC;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; BREAST NEOPLASMS; COMPUTER SIMULATION; CONTRAST MEDIA; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; KINETICS; MAGNETIC RESONANCE IMAGING; MEGLUMINE; METABOLIC CLEARANCE RATE; MIDDLE AGED; MODELS, BIOLOGICAL; ORGANOMETALLIC COMPOUNDS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; YOUNG ADULT;

EID: 84867832213     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.23746     Document Type: Article

References (40)

1. Mann RM, Kuhl CK, Kinkel K, Boetes C,. Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol 2008; 18: 1307-1318.
2. Schnall MD, Ikeda DM,. Lesion diagnosis working group report. J Magn Reson Imaging 1999; 10: 982-990.
3. Nunes LW, Schnall MD, Orel SG,. Update of breast MR imaging architectural interpretation model. Radiology 2001; 219: 484-494.
4. Schnall MD, Rosten S, Englander S, Orel SG, Nunes LW,. A combined architectural and kinetic interpretation model for breast MR images. Acad Radiol 2001; 8: 591-597.
5. Schnall MD, Blume J, Bluemke DA, et al. Diagnostic architectural and dynamic features at breast MR imaging: multicenter Study 1. Radiology 2006; 238: 42.
6. Kuhl C,. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 2007; 244: 356-378.
7. ™. 4th. Reston, VA: American College of Radiology; 2006.
8. Stoutjesdijk MJ, Futterer JJ, Boetes C, van Die LE, Jager G, Barentsz JO,. Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Invest Radiol 2005; 40: 355-362.
9. Mussurakis S, Buckley DL, Horsman A,. Dynamic MR imaging of invasive breast cancer: correlation with tumour grade and other histological factors. Br J Radiol 1997; 70: 446.
10. Kuhl CK, Schild HH, Morakkabati N,. Dynamic bilateral contrast-enhanced MR imaging of the breast: trade-off between spatial and temporal resolution. Radiology 2005; 236: 789-800.
11. Liney GP, Sreenivas M, Gibbs P, Garcia-Alvarez R, Turnbull LW,. Breast lesion analysis of shape technique: semiautomated vs. manual morphological description. J Magn Reson Imaging 2006; 23: 493-498.
12. Stoutjesdijk MJ, Veltman J, Huisman H, et al. Automated analysis of contrast enhancement in breast MRI lesions using mean shift clustering for ROI selection. J Magn Reson Imaging 2007; 26: 606-614.
13. Metz CE. ROCKIT (computer program), version 1.0.1 beta2 [Internet]. ROCKIT (computer program), version 1.0.1 beta2. [cited 2010, Dec 9] Available at: http://metz-roc.uchicago.edu/. Accessed April 28, 2012.
14. Otsu N,. A threshold selection method from Gray-level histograms. IEEE Trans Syst Man Cybern 1979; 9: 62-66.
15. Fukunaga K, Hostetler L,. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 1975; 21: 32-40.
16. Comaniciu D, Meer P,. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell. 2002; 24: 603-619.
17. Montgomery DC,. Response surface methods and designs. In: Design and analysis of experiments. Hoboken, NJ: Wiley; 2009. p 417-485.
18. Cortes C, Vapnik V,. Support-vector networks. Mach learn 1995; 20: 273-297.
19. Hsu CW, Chang CC, Lin CJ,. A practical guide to support vector classification [Internet]. April 15, 2010. Available at: http://www.csie.ntu. edu.tw/∼cjlin. Accessed April 28, 2012.
20. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A,. Package 'e1071' [Internet]. Available at: http://cran.r-project.org/web/packages/e1071/ e1071.pdf. Accessed April 28, 2012.
21. Huisman HJ, Engelbrecht MR, Barentsz JO,. Accurate estimation of pharmacokinetic contrast-enhanced dynamic MRI parameters of the prostate. J Magn Reson Imaging 2001; 13: 607-614.
22. Kovar DA, Lewis M, Karczmar GS,. A new method for imaging perfusion and contrast extraction fraction: input functions derived from reference tissues. J Magn Reson Imaging 1998; 8: 1126-1134.
23. Lin M, Chen JH, Nie K, Chang D, Nalcioglu O, Su MY,. Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis. J Magn Reson Imaging 2009; 30: 817-824.
24. Chen W, Giger ML, 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.
25. Simmons A, Arridge SR, Barker GJ, Williams SC,. Simulation of MRI cluster plots and application to neurological segmentation. Magn Reson Imaging 1996; 14: 73-92.
26. Jacobs MA, Barker PB, Bluemke DA, et al. Benign and malignant breast lesions: diagnosis with multiparametric MR imaging. Radiology 2003; 229: 225-232.
27. Forbes F, Peyrard N, Fraley C, Georgian-Smith D, Goldhaber DM, Raftery AE,. Model-based region-of-interest selection in dynamic breast MRI. J Comput Assist Tomogr 2006; 30: 675-687.
28. Chen W, Giger ML, Bick U, Newstead GM,. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 2006; 33: 2878-2887.
29. Leinsinger G, Schlossbauer T, Scherr M, Lange O, Reiser M, Wismuller A,. Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions? Eur Radiol 2006; 16: 1138-1146.
30. Comaniciu D, Ramesh V, Meer P,. The variable bandwidth mean shift and data-driven scale selection. In: Computer Vision, 2001. ICCV 2001. In: Proceedings of the 8th IEEE International Conference, 2002. p 438-445.
31. Comaniciu D,. An algorithm for data-driven bandwidth selection. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2003; 25: 281-288.
32. Georgescu B, Shimshoni I, Meer P,. Mean shift based clustering in high dimensions: a texture classification example. In: Computer Vision, 2003. Proceedings of the 9th IEEE International Conference, 2008. p 456-463.
33. Bhooshan N, Giger ML, Jansen SA, Li H, Lan L, Newstead GM,. Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology 2010; 254: 680-690.
34. Chen W, Giger ML, Li H, Bick U, Newstead GM,. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 2007; 58: 562-571.
35. Gibbs P, Turnbull LW,. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003; 50: 92-98.
36. Gilhuijs KG, Giger ML, Bick U,. Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys 1998; 25: 1647-1654.
37. Schabel MC, Morrell GR, Oh KY, Walczak CA, Barlow RB, Neumayer LA,. Pharmacokinetic mapping for lesion classification in dynamic breast MRI. J Magn Reson Imaging 2010; 31: 1371-1378.
38. Huang W, Li X, Morris EA, et al. The magnetic resonance shutter speed discriminates vascular properties of malignant and benign breast tumors in vivo. Proc Natl Acad Sci U S A 2008; 105: 17943-17948.
39. Wiener JI, Schilling KJ, Adami C, Obuchowski NA,. Assessment of suspected breast cancer by MRI: a prospective clinical trial using a combined kinetic and morphologic analysis. AJR Am J Roentgenol 2005; 184: 878-886.
40. Veltman J, Mann RM, Meijer FJ, et al. The additional value of three time point color coding in dynamic contrast-enhanced MRI of the breast for inexperienced and experienced readers. Eur J Radiol 2010; 74: 514-518.