×

zbMATH — the first resource for mathematics

Bayesian analysis of dynamic magnetic resonance breast images. (English) Zbl 1111.92301
Summary: We describe an integrated methodology for analysing dynamic magnetic resonance images of the breast. The problems that motivate this methodology arise from a collaborative study with a tumour institute. The methods are developed within the Bayesian framework and comprise image restoration and classification steps. Two different approaches are proposed for the restoration. Bayesian inference is performed by means of Markov chain Monte Carlo algorithms. We make use of a Metropolis algorithm with a specially chosen proposal distribution that performs better than more commonly used proposals. The classification step is based on a few attribute images yielded by the restoration step that describe the essential features of the contrast agent variation over time. Procedures for hyperparameter estimation are provided, so making our method automatic. The results show the potential of the methodology to extract useful information from acquired dynamic magnetic resonance imaging data about tumour morphology and internal pathophysiological features.

MSC:
92C55 Biomedical imaging and signal processing
62F15 Bayesian inference
65C40 Numerical analysis or methods applied to Markov chains
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Besag J., J. R. Statist. Soc. 48 pp 259– (1986)
[2] Besag J., J. Appl. Statist. 16 pp 395– (1989)
[3] DOI: 10.1214/ss/1028905934 · Zbl 0966.65004 · doi:10.1214/ss/1028905934
[4] Geman S., IEEE Trans. Pattn Anal. Mach. Intell. 6 pp 721– (1984)
[5] Gilks W. R., Markov Chain Monte Carlo in Practice (1996) · Zbl 0832.00018 · doi:10.1007/978-1-4899-4485-6
[6] Glad I. K., Biometrika 82 pp 237– (1995)
[7] Glasbey C. A., Image Analysis for the Biological Sciences (1995) · Zbl 0876.92001
[8] Green P. J., J. Am. Statist. Ass. 97 pp 1055– (2002)
[9] Gribbestad I., Acta Oncol. 31 pp 833– (1992)
[10] DOI: 10.1016/S0004-3702(99)00073-9 · Zbl 0939.68849 · doi:10.1016/S0004-3702(99)00073-9
[11] DOI: 10.1016/S1361-8415(97)85011-6 · doi:10.1016/S1361-8415(97)85011-6
[12] DOI: 10.1118/1.595711 · doi:10.1118/1.595711
[13] Heywang-Kobrunner S. H., Contrast Enhanced MRI of the Breast (1995)
[14] Highnam R., Mammographic Image Analysis (1999) · Zbl 1057.68714 · doi:10.1007/978-94-011-4613-5
[15] DOI: 10.1118/1.598576 · doi:10.1118/1.598576
[16] Kuhl C. K., Radiology 211 pp 101– (1999) · doi:10.1148/radiology.211.1.r99ap38101
[17] Kunsch H. R., Ann. Inst. Statist. Math. 46 pp 1– (1994)
[18] Marroquin J., J. Am. Statist. Ass. 82 pp 76– (1987)
[19] Metropolis N., J. Chem. Phys. 21 pp 1087– (1953)
[20] DOI: 10.1097/00004728-199705000-00017 · doi:10.1097/00004728-199705000-00017
[21] DOI: 10.1097/00004728-199801000-00007 · doi:10.1097/00004728-199801000-00007
[22] Potts R. B., Proc. Camb. Philos. Soc. 48 pp 106– (1952)
[23] DOI: 10.1016/S0165-1684(97)00002-9 · Zbl 1008.68505 · doi:10.1016/S0165-1684(97)00002-9
[24] DOI: 10.1080/10485250211384 · Zbl 1014.62111 · doi:10.1080/10485250211384
[25] DOI: 10.1002/(SICI)1098-1098(1999)10:2<109::AID-IMA2>3.0.CO;2-R · doi:10.1002/(SICI)1098-1098(1999)10:2<109::AID-IMA2>3.0.CO;2-R
[26] Villringer A., Magn. Reson. Med. 6 pp 164– (1988)
[27] Weinstein D., Radiology 210 pp 233– (1999) · doi:10.1148/radiology.210.1.r99ja18233
[28] Winkler G., Image Analysis, Random Fields and Dynamic Monte Carlo Methods (1995) · Zbl 0821.68125 · doi:10.1007/978-3-642-97522-6
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.