Application of artificial neural networks for tissue classification from multispectral magnetic resonance images of the head

Schellenberg, J.D.; Naylor, W.C.; Clarke, Laurence P.

doi:10.1109/cbmsys.1990.109419

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…If the features are MR signal intensities from various MRI modalities (such as T1, T2, PD), then the Gaussian model assumption can be poor (Clarke et al, 1993;DeCarli et al, 1992;Schellenberg et al, 1990) : besides biological causes such as the intrinsic heterogeneity within the tissue classes that concern this paper (CSF, grey matter, white matter), the MRI acquisition artifacts also affect the intensity distributions -i.e. result in deviations from a Normal distribution (Ashburner, 2000;Kollokian, 1996;Schellenberg et al, 1990). While intensity non-uniformity can be reduced by retrospective correction methods (e.g.…”

Section: Feature Space Distributionsmentioning

confidence: 99%

A fully automatic and robust brain MRI tissue classification method

Cocosco

Zijdenbos

Evans

2003

Medical Image Analysis

299

184

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A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a "pruning" strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a "model") in a standard, brain-based coordinate system ("stereotaxic space"), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.

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Section: Feature Space Distributionsmentioning

confidence: 99%

A fully automatic and robust brain MRI tissue classification method

Cocosco

Zijdenbos

Evans

2003

Medical Image Analysis

299

184

View full text Add to dashboard Cite

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Review of neural network applications in medical imaging and signal processing

Miller

Blott

Hames

1992

Med. Biol. Eng. Comput.

301

107

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The current applications of neural networks to in vivo medical imaging and signal processing are reviewed. As is evident from the literature neural networks have already been used for a wide variety of tasks within medicine. As this trend is expected to continue this review contains a description of recent studies to provide an appreciation of the problems associated with implementing neural networks for medical imaging and signal processing.

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Towards an efficient segmentation of small rodents brain: A short critical review

Feo

Giove

2019

Journal of Neuroscience Methods

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Application of artificial neural networks for tissue classification from multispectral magnetic resonance images of the head

Cited by 6 publications

References 9 publications

A fully automatic and robust brain MRI tissue classification method

A fully automatic and robust brain MRI tissue classification method

Review of neural network applications in medical imaging and signal processing

Towards an efficient segmentation of small rodents brain: A short critical review

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