Automatic Segmentation of Pressure Images Acquired in a Clinical Setting
One of the major obstacles to pressure ulcer research is the difficulty in accurately measuring mechanical loading of specific anatomical sites. A human motion analysis system capable of automatically segmenting a patient's body into high-risk areas can greatly improve the ability of researche...
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| Format: | Others |
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VCU Scholars Compass
2013
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| Online Access: | http://scholarscompass.vcu.edu/etd/502 http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1501&context=etd |
| Summary: | One of the major obstacles to pressure ulcer research is the difficulty in accurately measuring mechanical loading of specific anatomical sites. A human motion analysis system capable of automatically segmenting a patient's body into high-risk areas can greatly improve the ability of researchers and clinicians to understand how pressure ulcers develop in a hospital environment. This project has developed automated computational methods and algorithms to analyze pressure images acquired in a hospital setting. The algorithm achieved 99% overall accuracy for the classification of pressure images into three pose classes (left lateral, supine, and right lateral). An applied kinematic model estimated the overall pose of the patient. The algorithm accuracy depended on the body site, with the sacrum, left trochanter, and right trochanter achieving an accuracy of 87-93%. This project reliably segments pressure images into high-risk regions of interest. |
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