Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Case-Based Tissue Classification for Monitoring Leg Ulcer Healing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Multiple Resolution Texture Analysis and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-dimensional color histograms for segmentation of wounds in images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Recognition technology for the detection of buried land mines
IEEE Transactions on Fuzzy Systems
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
In this study, the different phases of pressure sore generation and healing are investigated through a combined analysis of high-frequency ultrasound (20MHz) images and digital color photographs. Pressure sores were artificially induced in guinea pigs, and the injured regions were monitored for 21 days (data were obtained on days 3, 7, 14, and 21). Several statistical features of the images were extracted, relating to both the altering pattern of tissue and its superficial appearance. The features were grouped into five independent categories, and each category was used to train a neural network whose outputs were the four days. The outputs of the five classifiers were then fused using a fuzzy integral to provide the final decision. We demonstrate that the suggested method provides a better decision regarding tissue status than using either imaging technique separately. This new approach may be a viable tool for detecting the phases of pressure sore generation and healing in clinical settings.