Name
Abstract | ||
|---|---|---|
Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue with high prevalence rates in aged people. Diagnosis and treatment of pressure ulcers involve high costs for sanitary systems. Accurate wound-state evaluation is a critical task for optimizing the effectiveness of treatments. Reliable trace of wound-state evolution can be done by precisely registering the wound area. Clinicians estimate the wound area with often subjective and imprecise manual methods. This article presents a computer-vision approach based on machine hybrid-learning techniques to precise automatic estimation of wound dimensions on pressure ulcer real images taken under non-controlled illumination conditions. The system combines neural networks and Bayesian classifiers to effectively recognize and separate skin and healing regions from wound-tissue regions to be measured. This tissue-recognition approach to wound area estimation gives high performance rates and operates better than a widespread clinical method when approximating real wound areas of variable size. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02478-8_131 | IWANN (1) |
Keywords | Field | DocType |
accurate wound-state evaluation,pressure ulcer,pressure ulcer real image,approximating real wound area,high cost,tissue recognition approach,high performance rate,wound area,neural networks,wound dimension,area estimation,high prevalence rate,pressure ulcer area estimation,clinical pathology,neural network,bayesian classifier,computer vision | Computer science,Artificial intelligence,Real image,Artificial neural network,Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
5517 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco J. Veredas | 1 | 58 | 12.13 |
| Héctor Mesa | 2 | 13 | 4.31 |
| Laura Morente | 3 | 46 | 6.01 |