Segmentation of MR images with intensity inhomogeneities

Rajapakse, Jagath C.; Kruggel, Frithjof

doi:10.1016/s0262-8856(97)00067-x

Cited by 132 publications

(76 citation statements)

References 38 publications

(67 reference statements)

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“…Although our interest in PVE is not in improving hard segmentations by modeling of it, it is interesting to compare the results of our method to those obtained with algorithms for hard segmentation. Rajapakse and Krugge (1998) compared several algorithms and the best TC values were obtained by Adaptive MAP method, and they were 0.567 for the WM and 0.564 for the GM. A more recent method by Marroquin et al (2002) achieved TCs 0.683 for the WM and 0.662 for the GM.…”

Section: Results With the Ibsr Data Setmentioning

confidence: 99%

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

2004

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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.

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Section: Results With the Ibsr Data Setmentioning

confidence: 99%

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

2004

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“…But certain subcortical structure like thalamus has diffuse gray level intensity, which makes the tissue segmentation inaccurate. A more intelligent tissue segmentation will enhance system performance (Rajapakse and Kruggel, 1998). Future work can extract more useful information, besides the tissue type information, from the test image itself to detect each subject's own uniqueness and subtle shape changes.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of subcortical brain structures using fuzzy templates

Zhou

Rajapakse

2005

NeuroImage

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“…There are methods [84][85][86][87][88] which consider the IIH as model parameters formulated in a segmentation framework. Suppose the segmentation is to optimize a functional Φ(y, θ, β), where y denotes the observed data, β the IIH term, and θ Rajapakse and Kruggel [84] used an MRF formulation, whereas Farag and his group exploited the fuzzy c-means clustering framework [85][86][87][88].…”

Section: Estimate Without Explicit Modelingmentioning

confidence: 99%

A Review on MR Image Intensity Inhomogeneity Correction

Hou

2006

International Journal of Biomedical Imaging

174

102

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Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Lowfrequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

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Segmentation of MR images with intensity inhomogeneities

Cited by 132 publications

References 38 publications

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

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

Segmentation of subcortical brain structures using fuzzy templates

A Review on MR Image Intensity Inhomogeneity Correction

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