nach oben

Medical & Biological Engineering & Computing

Erschienen in:

01.01.2015 | Original Article

A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity

verfasst von: Mei Xie, Jingjing Gao, Chongjin Zhu, Yan Zhou

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 1/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining a modified MRF classification and bias field estimation in an energy minimization framework, whose initial estimation is based on k-means algorithm in view of prior information on MRI. The proposed method has a salient advantage of overcoming the misclassifications from the non-interleaved MRF classification for the MR image with intensity inhomogeneity. In contrast to other baseline methods, experimental results also have demonstrated the effectiveness and advantages of our algorithm via its applications in the real and the synthetic MR images.

Vorheriger Artikel A new layered sensor for simultaneous measurement of EMG, MMG and oxygen consumption at the same position

Nächster Artikel Thalamic reticular cells firing modes and its dependency on the frequency and amplitude ranges of the current stimulus

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851PubMedCrossRef

Bottou L, Bengio Y (1995) Convergence properties of the k-means algorithms. Adv Neural Inf Process Syst 7

Brandt ME, Bohan TP, Kramer LA, Fletcher JM (1994) Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Comput Med Imaging Graph 18(1):5–34

Chen Z, Li SS, Yang J, Letizia D, Shen J (2004) Measurement and automatic correction of high-order B0 inhomogeneity in the rat brain at 11.7 Tesla. Magn Reson Imaging 22:835–842PubMedCrossRef

Cocosco CA, Kollokian V, Kwan RK, Evans AC (1997) BrainWeb: online interface to a 3D MRI simulated brain database. Neuroimage 5(4):2–4

Davatzikos CA, Prince JL (1995) An active contour model for mapping the cortex. Med Imaging IEEE Trans 14(1):65–80CrossRef

Derin H, Elliott H, Cristi R, Geman D (1984) Bayes smoothing algorithms for segmentation of binary images modeled by Markov random fields. IEEE Trans Pattern Anal Mach Intell (6):707–720

Desco M, Gispert JD, Reig S (2001) Statistical segmentation of multidimensional brain datasets, medical imaging. Int Soc Opt Photonics 2001:184–193

Friston KJ, Ashburner AP (2006) Statistical parametric mapping: the analysis of functional brain images. Academic Press, Waltham

10.

Hammersley JM, Clifford P (1971) Markov fields on finite graphs and lattices[M]. Oxford University, Oxford

11.

Held K, Kops ER, Krause BJ (1997) Markov random field segmentation of brain MR images. Med Imaging IEEE Trans 16(6):878–886CrossRef

12.

Li SZ (2009) Markov random field modeling in image analysis. Springer, Berlin

13.

Liew AWC, Yan H (2006) Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images. Curr Med Imaging Rev 2(1):91–103CrossRef

14.

Li C, Gatenby C, Wang L, Gore JC (2009) A robust parametric method for bias field estimation and segmentation of MR images. In: IEEE conference computer vision and pattern recognition, pp 218–223

15.

Li C, Huang R, Ding Z, Gatenby C, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016

16.

Likar B, Viergever MA, Pernus F (2001) Restrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging 20(12):1398–1410PubMedCrossRef

17.

Pham D, Prince J (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging 18(9):737–752 PubMedCrossRef

18.

Rajapakse JC, Kruggel F (1998) Segmentation of MR images with intensity inhomogeneities. Image Vis Comput 16(3):165–180CrossRef

19.

Sansone M, Zeni O, Esposito G (2012) Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering. Med Biol Eng Comput 50(5):523–532PubMedCrossRef

20.

Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM (2001) Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13:856–876PubMedCrossRef

21.

Simmons A, Tofts PS, Barker GJ, Arridge SR (1994) Sources of intensity nonuniformity in spin echo images at 1.5 T. Magn Reson Med 32:121–128PubMedCrossRef

22.

Sled J, Zijdenbos A, Evans A (1998) A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Trans Med Imaging 17(1):87–97PubMedCrossRef

23.

Suzuki H, Toriwaki J (1991) Automatic segmentation of head MRI images by knowledge guided thresholding. Comput Med Imaging Graph 15(4):233PubMedCrossRef

24.

Takao H, Abe O, Ohtomo K (2010) Computational analysis of cerebral cortex. Neuroradiology 52(8):691–698PubMedCrossRef

25.

Vovk U, Pernus F, Likar B (2007) A review of method for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3):405–421PubMedCrossRef

26.

Wang L, Li C, Sun Q, Xia D, Kao CY (2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 33(7):520–531PubMedCrossRef

27.

Wells W, Grimson E, Kikinis R, Jolesz F (1996) Adaptive segmentation of mri data. IEEE Trans Med Imaging 285 15(4):429–442CrossRef

28.

Wicks DA, Barker GJ, Tofts PS (1993) Correction of intensity nonuniformity in mr images of any orientation. Magn Reson Imaging 11(2):183–196PubMedCrossRef

29.

Yoon SW (2004) Medical endoscopic image segmentation using snakes. IEICE Trans Inf Syst 87(3):785–789

30.

Zhang Y (2007) A novel medical image segmentation method using dynamic programming. International conference on medical information visualisation-biomedical visualisation

31.

Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans Med Imaging 20(1):45–57PubMedCrossRef

Titel: A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity
verfasst von: Mei Xie
Jingjing Gao
Chongjin Zhu
Yan Zhou
Publikationsdatum: 01.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 1/2015
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-014-1198-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2015

Online apnea–bradycardia detection based on hidden semi-Markov models

The effect of energy spectrum change on DNA damage in and out of field in 10-MV clinical photon beams

A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means

Application of multiscale entropy in arterial waveform contour analysis in healthy and diabetic subjects

Thalamic reticular cells firing modes and its dependency on the frequency and amplitude ranges of the current stimulus

Feasibility of monitoring muscle health in microgravity environments using Myoton technology

Premium Partner