12-04-2012, 01:26 PM
ENHANCED ASSESSMENT OF THE WOUND-HEALING PROCESS BY ACCURATE MULTIVIEW TISSUE CLASSIFICATION
ABSTRACT:
A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, or friction. Diagnosis, treatment, and care of pressure ulcers are costly for health services.
Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem.
In this paper, a hybrid approach based on neural networks and Bayesian classifiers is used in the design of a computational system for automatic tissue identification in wound images. We focus here on tissue classification from color and texture region descriptors computed after unsupervised segmentation. Due to perspective distortions, uncontrolled lighting conditions and view points, wound assessments vary significantly between patient examinations.
The experimental classification tests demonstrate that enhanced repeatability and robustness are obtained and that metric assessment is achieved through real area and volume measurements and wound outline extraction.