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 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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2013

Author dc:creator

• Pepperl, Anathea

Contributors dc:contributor

• Paul Wetzel

• image segmentation
• pressure ulcer
• pressure mapping
• image analysis
• Biomedical Engineering and Bioengineering
• Engineering

Statement dc:rights

• © The Author

Identifier

https://scholarscompass.vcu.edu/etd/502

OAI identifier oai:identifier

oai:scholarscompass.vcu.edu:etd-1501