Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

Laidlaw, David H.; Fleischer, Kurt; Barr, Alan H.

doi:10.1109/42.668696

Cited by 168 publications

(99 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, a number of researchers have proposed continuous classifiers which attempt to estimate the mixing proportions of several tissues in a voxel (i.e. the partial volume effect) (Choi et al, 1991;Laidlaw et al, 1998;Pham and Prince, 1999;Schroeter et al, 1998;Van Leemput et al, 2002). Another approach, in which the limitations of intensity-based classification are addressed by constraining it with the non-linearly deformed anatomical template, was proposed by Warfield et al (2000).…”

Section: Introductionmentioning

confidence: 99%

A fully automatic and robust brain MRI tissue classification method

Cocosco

Zijdenbos

Evans

2003

Medical Image Analysis

297

184

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

A fully automatic and robust brain MRI tissue classification method

Cocosco

Zijdenbos

Evans

2003

Medical Image Analysis

297

184

View full text Add to dashboard Cite

show abstract

“…The method involves maximum-likelihood estimation of the PVCs for each voxel that model PV fractions of pure tissue types. Some authors have studied the identification of voxels containing PVE based on the mixel or a closely related model without trying to estimate the PVCs for each voxel (Laidlaw et al, 1998;Ruan et al, 2000;Santago and Gage, 1993). Our interest in this study is in estimating PVCs and not in merely identifying voxels containing PVE.…”

Section: Introductionmentioning

confidence: 99%

Fast and robust parameter estimation for statistical partial volume models in brain MRI

2004

View full text Add to dashboard Cite

Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation -maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface. D 2004 Elsevier Inc. All rights reserved.

show abstract

“…Laidlaw et al [11] use Bayes' theorem on a small neighborhood of a voxel to classify mixed materials. Tenginakai et al [24,25] introduced a method to extract salient iso-surfaces based on statistical methods.…”

Section: Related Workmentioning

confidence: 99%

Volume visualization based on statistical transfer-function spaces

Haidacher

Patel

Brückner

et al. 2010

2010 IEEE Pacific Visualization Symposium (PacificVis)

View full text Add to dashboard Cite

It is a difficult task to design transfer functions for noisy data. In traditional transfer-function spaces, data values of different materials overlap. In this paper we introduce a novel statistical transferfunction space which in the presence of noise, separates different materials in volume data sets. Our method adaptively estimates statistical properties, i.e. the mean value and the standard deviation, of the data values in the neighborhood of each sample point. These properties are used to define a transfer-function space which enables the distinction of different materials. Additionally, we present a novel approach for interacting with our new transferfunction space which enables the design of transfer functions based on statistical properties. Furthermore, we demonstrate that statistical information can be applied to enhance visual appearance in the rendering process. We compare the new method with 1D, 2D, and LH transfer functions to demonstrate its usefulness.

show abstract

Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

Cited by 168 publications

References 19 publications

A fully automatic and robust brain MRI tissue classification method

A fully automatic and robust brain MRI tissue classification method

Fast and robust parameter estimation for statistical partial volume models in brain MRI

Volume visualization based on statistical transfer-function spaces

Contact Info

Product

Resources

About