Eigenimage filtering in MR imaging: An application in the abnormal chest wall

Haggar, Allan M.; Windham, Joe P.; Reimann, David A.; Hearshen, David

doi:10.1002/mrm.1910110108

Cited by 23 publications

(9 citation statements)

References 6 publications

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“…EI image analysis was performed using a SUN UltraSPARC 60 workstation (Sun Microsystems Inc., Mountain View, CA, USA) (10, 13, 14). The EI filter technique is based on enhancement and suppression of different tissue types using signature vectors derived from the pure tissues.…”

Section: Methodsmentioning

confidence: 99%

Fast method for brain image segmentation: Application to proton magnetic resonance spectroscopic imaging

Bonekamp¹,

Horská²,

Jacobs³

et al. 2005

Magnetic Resonance in Med

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The interpretation of brain metabolite concentrations measured by quantitative proton magnetic resonance spectroscopic imaging (MRSI) is assisted by knowledge of the percentage of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) within each MRSI voxel. Usually, this information is determined from T(1)-weighted magnetic resonance images (MRI) that have a much higher spatial resolution than the MRSI data. While this approach works well, it is time-consuming. In this article, a rapid data acquisition and analysis procedure for image segmentation is described, which is based on collection of several, thick slice, fast spin echo images (FSE) of different contrast. Tissue segmentation is performed with linear "Eigenimage" filtering and normalization. The method was compared to standard segmentation techniques using high-resolution 3D T(1)-weighted MRI in five subjects. Excellent correlation between the two techniques was obtained, with voxel-wise regression analysis giving GM: R2 = 0.893 +/- 0.098, WM: R2 = 0.892 +/- 0.089, ln(CSF): R2 = 0.831 +/- 0.082). Test-retest analysis in one individual yielded an excellent agreement of measurements with R2 higher than 0.926 in all three tissue classes. Application of FSE/EI segmentation to a sample proton MRSI dataset yielded results similar to prior publications. It is concluded that FSE imaging in conjunction with Eigenimage analysis is a rapid and reliable way of segmenting brain tissue for application to proton MRSI.

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Section: Methodsmentioning

confidence: 99%

Fast method for brain image segmentation: Application to proton magnetic resonance spectroscopic imaging

Bonekamp¹,

Horská²,

Jacobs³

et al. 2005

Magnetic Resonance in Med

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show abstract

“…The matched filter does not address the issue of differential noise and contrast levels, for various tissues and pulse sequences, and therefore it is not optimal for enhancing classification accuracy. The Eigenimage method, which is described in 9 , and about which further detail is supplied in 10 , attempts to produce a feature which maximizes the signal from a desired tissue, while suppressing signal from one or more interfering tissues. The authors of 29 describe a technique to maximize contrast to noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Improved classification accuracy by feature extraction using genetic algorithms

2003

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A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation -the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

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“…One early example is the orthogonal subspace projection ͑OSP͒ approach, which has shown great success in hyperspectral image classification. [3][4][5][6][7] It was derived from an eigenimaging approach, [8][9][10][11][12][13] which is based on the ratio of a desired feature energy to undesired feature energies, a criterion similar to the signal-to-noise ratio ͑SNR͒. More recently, Soltanian-Zadeh et al developed a constrained criterion for MRI to characterize brain issues for 3-D feature representation.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised orthogonal subspace projection approach to magnetic resonance image classification

Wang

Chen²,

et al. 2002

Opt. Eng

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MR images and remotely sensed images share similar image structures and characteristics because they are acquired remotely as image sequences by spectral channels at different wavelengths. As a result, techniques developed for one may be also applicable to the other. In the past, we have witnessed that some techniques that were developed for magnetic resonance imaging (MRI) found great success in remote sensing image applications. Unfortunately, the opposite direction is yet to be investigated. In this paper, we present an application of one successful remote sensing image classification technique, called orthogonal subspace projection (OSP), to magnetic resonance image classification. Unlike classical image classification techniques, which are designed on a pure pixel basis, OSP is a mixed pixel classification technique that models an image pixel as a linear mixture of different material substances assumed to be present in the image data, then estimates the abundance fraction of each individual material substance within a pixel for classification. Technically, such mixed pixel classification is performed by estimating the abundance fractions of material substances resident in a pixel, rather than assigning a class label to it as usually done in pure-pixel-based classification techniques such as a minimum-distance or nearest-neighbor rule. The advantage of mixed pixel classification has been demonstrated in many applications in remote sensing image processing. The MRI experiments reported in this paper further show that mixed pixel classification may have advantages over the pure pixel classification.

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Eigenimage filtering in MR imaging: An application in the abnormal chest wall

Cited by 23 publications

References 6 publications

Fast method for brain image segmentation: Application to proton magnetic resonance spectroscopic imaging

Fast method for brain image segmentation: Application to proton magnetic resonance spectroscopic imaging

Improved classification accuracy by feature extraction using genetic algorithms

Unsupervised orthogonal subspace projection approach to magnetic resonance image classification

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