&lt;title&gt;Validation of brain segmentation and tissue classification algorithm for T1-weighted MR images&lt;/title&gt;

Chalana, Vikram; Ng, Lydia; Rystrom, Larry R.; Gee, James C.; Haynor, David R.

doi:10.1117/12.431079

Cited by 4 publications

(4 citation statements)

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“…Few algorithms rely solely on image intensity, (Schnack 2001) because these approaches are overly sensitive to image artifacts such as radiofrequency inhomogeneity, B 0 inhomogeneity, and aliasing, and can not adequately account for overlapping intensity distributions across structures. Therefore, to improve segmentation accuracy, most tissue segmentation algorithms combine intensity information with other techniques, such as the use of a priori anatomic information (Chalana 2001; Van Leemput 1999) or edge information through deformable contours (Davatzikos 1995; Xu 1999; Zeng 1999). Intensity information is analyzed differently in each approach, including Gaussian mixture models (Ashburner 2005; Andersen 2002; Marroquin 2002; Zhang 2001), discriminant analysis (Amato 2003), k-nearest neighbor classification (Mohamed 1999), and fuzzy c-means clustering (Pham 1999; Suckling 1999; Ahmed 2002; Zhu 2003; Zhou 2007).…”

Section: Introductionmentioning

confidence: 99%

Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients

Nakamura

Fisher

2009

NeuroImage

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Multiple sclerosis (MS) affects both white matter and gray matter (GM). Measurement of GM volumes is a particularly useful method to estimate the total extent of GM tissue damage because it can be done with conventional magnetic resonance images (MRI). Many algorithms exist for segmentation of GM, but none were specifically designed to handle issues associated with MS, such as atrophy and the effects that MS lesions may have on the classification of GM. A new GM segmentation algorithm has been developed specifically for calculation of GM volumes in MS patients. The new algorithm uses a combination of intensity, anatomical, and morphological probability maps. Several validation tests were performed to evaluate the algorithm in terms of accuracy, reproducibility, and sensitivity to MS lesions. The accuracy tests resulted in error rates of 1.2% and 3.1% for comparisons to BrainWeb and manual tracings, respectively. Similarity indices indicated excellent agreement with the BrainWeb segmentation (0.858-0.975, for various levels of noise and rf inhomogeneity). The scan-rescan reproducibility test resulted in a mean coefficient of variation of 1.1% for GM fraction. Tests of the effects of varying the size of MS lesions revealed a moderate and consistent dependence of GM volumes on T2 lesion volume, which suggests that GM volumes should be corrected for T2 lesion volumes using a simple scale factor in order to eliminate this technical artifact. The new segmentation algorithm can be used for improved measurement of GM volumes in MS patients, and is particularly applicable to retrospective datasets.

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Section: Introductionmentioning

confidence: 99%

Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients

Nakamura

Fisher

2009

NeuroImage

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“…We also do further clustering to confirm these findings. All the silhouette values using the correlation metric are greater than 0.80, which means strong structures have been found (Chalana et al, 2001). The case with k = 6 has the highest possible mean of silhouette values (1.0000), and the numbers of voxels in the six clusters are 102, 86, 48, 58, 45 and 1.…”

Section: Data Analysis For the First Saccade Data Setmentioning

confidence: 96%

Sparse geostatistical analysis in clustering fMRI time series

Lazar

2011

Journal of Neuroscience Methods

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“…To measure the changes over time in GM volumes, accurate segmentation methods must be used. A variety of different approaches to brain tissue segmentation has been described in the literature [2][3][4]. Few algorithms rely solely on image intensity, [2] because these approaches are overly sensitive to image artifacts such as radio frequency inhomogeneity, and aliasing, and cannot adequately account for overlapping intensity distributions across structures.…”

Section: Introductionmentioning

confidence: 99%

“…Few algorithms rely solely on image intensity, [2] because these approaches are overly sensitive to image artifacts such as radio frequency inhomogeneity, and aliasing, and cannot adequately account for overlapping intensity distributions across structures. Therefore, to improve segmentation accuracy, most tissue segmentation algorithms combine intensity information with other techniques, such as the use of a priori anatomic information [3,4] or edge information through deformable contours. The use of multiple images has significant advantages over a single image because the different contrasts can be enhanced between tissues.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique Based on Combining Fuzzy K-means Clustering and Region Growing for Improving Gray Matter and White Matter Segmentation

Afifi¹

2012

IJACSA

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Abstract-In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM) and white matter (WM) tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG) method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results.

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<title>Validation of brain segmentation and tissue classification algorithm for T1-weighted MR images</title>

Cited by 4 publications

References 0 publications

Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients

Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients

Sparse geostatistical analysis in clustering fMRI time series

A Hybrid Technique Based on Combining Fuzzy K-means Clustering and Region Growing for Improving Gray Matter and White Matter Segmentation

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