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| Content Provider | PubMed Central |
|---|---|
| Author | Prescott, Jeffrey W. Priddy, Mike Best, Thomas M. Pennell, Michael Swanson, Mark S. Haq, Furqan Jackson, Rebecca D. Gurcan, Metin N. |
| Abstract | In this paper we explore a method of segmentation of muscle interstitial adipose tissue (IAT) in MR images of the thigh. The objective is to apply the method towards research into biomarkers of osteoarthritis (OA). T1-weighted images of the thigh are intensity standardized through bias field correction and intensity normalization. IAT within the thigh muscles is then segmented using a threshold combined with morphological constraints applied on connected regions in the thresholded image. The morphological constraints can be adjusted to allow for highly sensitive or highly specific IAT segmentation. The use of the morphological constraints improved the specificity of IAT segmentation over a threshold segmentation method from 0.54 to 0.67, while retaining a nearly equivalent sensitivity of 0.82 compared to 0.84. We then present a preliminary statistical analysis to demonstrate the application of the automated IAT segmentation. Finally, we specify a protocol for further exploration of IAT by leveraging the massive imaging dataset of the Osteoarthritis Initiative (OAI). |
| Related Links | http://dx.doi.org/10.1109/IEMBS.2009.5333260 |
| Starting Page | 6360 |
| File Format | |
| ISSN | 1557170X |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
| Volume Number | 1 |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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