A nonparametric MRI inhomogeneity correction method

Manjón, José V.; Lull, Juan José; Carbonell-Caballero, José; García-Martí, Gracián; Martí‐Bonmatí, Luis; Robles, Montserrat

doi:10.1016/j.media.2007.03.001

Cited by 62 publications

(56 citation statements)

References 20 publications

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“…The current segmentation routine considers adipose inhomogeneity as a 3D artifact with neither specific origin nor form: 38 Unlike other techniques, it does not explicitly depend on breast symmetry to perform the correction. 37 Adipose nonuniformity was corrected in the new protocol using a nonparametric method initially developed for application to the slowly varying, positive, multiplicative bias field in MRI data with additive noise 38 and later applied by Yang et al 31 to bCT data,…”

Section: D Normalization and 3d Nonuniformity Correctionmentioning

confidence: 99%

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Population of 224 realistic human subject-based computational breast phantoms

et al. 2015

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Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230+ dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

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Section: D Normalization and 3d Nonuniformity Correctionmentioning

confidence: 99%

“…37 Adipose nonuniformity was corrected in the new protocol using a nonparametric method initially developed for application to the slowly varying, positive, multiplicative bias field in MRI data with additive noise 38 and later applied by Yang et al 31 to bCT data,…”

Section: D Normalization and 3d Nonuniformity Correctionmentioning

confidence: 99%

Population of 224 realistic human subject-based computational breast phantoms

et al. 2015

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“…To eliminate the artifacts from the image, we apply a Non Local Means filter, which proved its efficiency on the MRI [10]. Being given the image using method NLM the value filtered in a point is calculated like a weighted average of all the pixels of the image given by: (2) ℎ a parameter which allows the control of the filter, represents a constant of standardization, and d the Euclidean distance which is written:…”

Section: Materials and Methodmentioning

confidence: 99%

A New Hybrid Method for the Segmentation of the Brain MRIS

Abderrezak¹,

Chibane²,

Mansour³

2014

SIPIJ

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“…For these reasons, it is necessary to correct this inhomogeneity and there are several approaches, including modeling of field heterogeneity by DC basis functions (Ashburner and Friston, 2000), the use of Legendre polynomial basis functions (Brechbühler et al, 1996) or the Gaussian deconvolution on the histogram of the image (Sled et al, 1998). There are also methods which model the field by a linear combination of low frequency functions based on cubic B-splines adjusted by a cost function based on the intensity and the gradient of the image (Manjón et al, 2007).…”

Section: Signal Heterogeneitymentioning

confidence: 99%