Automatic muscle/fat quantification on MR images

Colin, Aurélien; Erbland, E.; Datin, C.; Boire, Jean-Yves; Veyre, A.; Zanca, Michel

doi:10.1109/iembs.1995.575209

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Therefore the automated quantification of muscle and adipose tissues is an active area of investigation. Earlier reports proposed semi-automated approaches that made use of simple histogram analysis and thresholding operations that typically had to be adjusted to compensate for inter-subject variability (7). Other notable works used fuzzy clustering followed by post-processing (8), segmentation of muscle and fat from whole-body MRI using manual seeding (9), semi-automated intensity scaling (10), a registration-segmentation collaborative quantification scheme for the abdomen (11), and more advanced model-based segmentation and Expectation-Maximization histogram analysis applied to single-slice non-suppressed T 1 -weighted images (12).…”

Section: Introductionmentioning

confidence: 99%

Automated quantification of muscle and fat in the thigh from water‐, fat‐, and nonsuppressed MR images

Makrogiannis

Serai

Fishbein

et al. 2011

Magnetic Resonance Imaging

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Purpose To introduce and validate an unsupervised muscle and fat quantification algorithm based on joint analysis of water-suppressed (WS), fat-suppressed (FS) and water-and-fat (non-suppressed) volumetric MR images of the mid-thigh region. Materials and Methods We first segment the subcutaneous fat by use of a parametric deformable model, and then apply centroid clustering in the feature domain defined by the voxel intensities in water- and fat-suppressed images to identify the inter-muscular fat and muscle. In the final step we compute volumetric and area measures of fat and muscle. We applied this algorithm on datasets of water-, fat- and non-suppressed volumetric MR images acquired from 28 participants. Results We validated our tissue composition analysis against fat and muscle area measurements obtained from semi-manual analysis of single-slice mid-thigh CT images of the same participants and found very good agreement between the two methods. Furthermore, we compared the proposed approach with a variant that uses non-suppressed images only and observed that joint analysis of water- and fat-suppressed images is more accurate than the non-suppressed only variant. Conclusion Our MRI algorithm produces accurate tissue quantification, is less labor intensive and more reproducible than the original CT-based workflow and can address inter-participant anatomic variability and intensity inhomogeneity effects.

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Section: Introductionmentioning

confidence: 99%

Automated quantification of muscle and fat in the thigh from water‐, fat‐, and nonsuppressed MR images

Makrogiannis

Serai

Fishbein

et al. 2011

Magnetic Resonance Imaging

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“…The analysis of MRI images can be used in the quantification of human body composition, such as the quantification of muscle/fat ratio [1], and assessment for variation of body fat content [2]. All this work has important medical significance in human nutrition and muscle physiology, in the study of pathologic consequences of obesity, and in the study of diseases of muscle [3].…”

Section: Introductionmentioning

confidence: 99%

An intelligent generalized system for tissue classification on MR images by integrating qualitative medical knowledge

Han¹,

Pinti²,

Taleb‐Ahmed³

et al. 2011

Biomedical Signal Processing and Control

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“…Therefore the automated quantification of muscle and adipose tissues is an active area of investigation. Earlier reports proposed semi-automated approaches that made use of simple histogram analysis and thresholding operations that typically had to be adjusted to compensate for inter-subject variability (7). Other notable works used fuzzy clustering followed by postprocessing (8), segmentation of muscle and fat from whole-body MRI using manual seeding (9), semi-automated intensity scaling (10), a registration-segmentation collaborative quantification scheme for the abdomen (11), and more advanced model-based segmentation and Expectation-Maximization histogram analysis applied to single-slice non-suppressed T 1weighted images (12).…”

Section: Introductionmentioning

confidence: 99%

Automated Quantification of Muscle and Fat in the Thigh from Water-, Fat- and Non-suppressed MR Images

Makrogiannis

Serai

Fishbein

et al. 2010

2010 IEEE International Conference on BioInformatics and BioEngineering

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Purpose-To introduce and validate an unsupervised muscle and fat quantification algorithm based on joint analysis of water-suppressed (WS), fat-suppressed (FS) and water-and-fat (nonsuppressed) volumetric MR images of the mid-thigh region. Materials and Methods-We first segment the subcutaneous fat by use of a parametric deformable model, and then apply centroid clustering in the feature domain defined by the voxel intensities in water-and fat-suppressed images to identify the inter-muscular fat and muscle. In the final step we compute volumetric and area measures of fat and muscle. We applied this algorithm on datasets of water-, fat-and non-suppressed volumetric MR images acquired from 28 participants. Results-We validated our tissue composition analysis against fat and muscle area measurements obtained from semi-manual analysis of single-slice mid-thigh CT images of the same participants and found very good agreement between the two methods. Furthermore, we compared the proposed approach with a variant that uses non-suppressed images only and observed that joint analysis of water-and fat-suppressed images is more accurate than the nonsuppressed only variant. Conclusion-Our MRI algorithm produces accurate tissue quantification, is less labor intensive and more reproducible than the original CT-based workflow and can address inter-participant anatomic variability and intensity inhomogeneity effects.

show abstract

Automatic muscle/fat quantification on MR images

Cited by 12 publications

References 3 publications

Automated quantification of muscle and fat in the thigh from water‐, fat‐, and nonsuppressed MR images

Automated quantification of muscle and fat in the thigh from water‐, fat‐, and nonsuppressed MR images

An intelligent generalized system for tissue classification on MR images by integrating qualitative medical knowledge

Automated Quantification of Muscle and Fat in the Thigh from Water-, Fat- and Non-suppressed MR Images

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