Image-based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization
| العنوان: | Image-based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization |
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| المؤلفون: | Kenneth W. Fishbein, Ann Zenobia Moore, Luigi Ferrucci, Richard G. Spencer, Sokratis Makrogiannis |
| المصدر: | IEEE Transactions on Biomedical Engineering. :1-1 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2015. |
| سنة النشر: | 2015 |
| مصطلحات موضوعية: | Computer science, Biomedical Engineering, Adipose tissue, Predictive capability, Thigh, Muscle mass, computer.software_genre, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Voxel, Image Processing, Computer-Assisted, medicine, Humans, Tissue distribution, Muscle, Skeletal, Aged, Models, Statistical, medicine.diagnostic_test, Skeletal muscle, Magnetic resonance imaging, Middle Aged, Magnetic Resonance Imaging, Characterization (materials science), medicine.anatomical_structure, Female, Functional status, computer, Algorithms, 030217 neurology & neurosurgery, Image based, Biomedical engineering |
| الوصف: | The identification and characterization of regional body tissues is essential to understand changes that occur with aging and age-related metabolic diseases such as diabetes and obesity and how these diseases affect trajectories of health and functional status. Imaging technologies are frequently used to derive volumetric, area, and density measurements of different tissues. Despite the significance and direct applicability of automated tissue quantification and characterization techniques, these topics have remained relatively under-explored in the medical image analysis literature. We present a method for identification and characterization of muscle and adipose tissue in the mid-thigh region using MRI. We propose an image-based muscle quality prediction technique that estimates tissue-specific probability density models and their eigenstructures in the joint domain of water- and fat-suppressed voxel signal intensities along with volumetric and intensity-based tissue characteristics computed during the quantification stage. We evaluated the predictive capability of our approach against reference biomechanical muscle quality measurements using statistical tests and classification performance experiments. The reference standard for muscle quality is defined as the ratio of muscle strength to muscle mass. The results show promise for the development of non-invasive image-based muscle quality descriptors. |
| تدمد: | 1558-2531 0018-9294 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ec228532620b3053a12dfca3408ddf2Test https://doi.org/10.1109/tbme.2015.2474305Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....6ec228532620b3053a12dfca3408ddf2 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15582531 00189294 |
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