Name
Abstract | ||
|---|---|---|
Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring. This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site. For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value. This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed.
201505164RIND
,
201803108RSB
. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12911-019-0813-0 | BMC Medical Informatics and Decision Making |
Keywords | Field | DocType |
Clustering, Edge detection, Image segmentation, Machine learning, Medical image processing, Surgical site classification, Wound assessment | Chronic wound,Data mining,Anomaly detection,Pattern recognition,Edge detection,Support vector machine,Image segmentation,Artificial intelligence,Cluster analysis,True positive rate,Medicine,Medical diagnosis | Journal |
Volume | Issue | ISSN |
19 | 1 | 1472-6947 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jui-Tse Hsu | 1 | 1 | 0.69 |
| Yung-Wei Chen | 2 | 3 | 2.56 |
| Te-Wei Ho | 3 | 19 | 5.51 |
| H. C. Tai | 4 | 0 | 0.68 |
| Jin-Ming Wu | 5 | 20 | 3.63 |
| Hsin-Yun Sun | 6 | 0 | 0.34 |
| Chi-Sheng Hung | 7 | 2 | 1.92 |
| Yi-Chong Zeng | 8 | 0 | 3.04 |
| Sy-Yen Kuo | 9 | 2304 | 245.46 |
| Feipei Lai | 10 | 846 | 81.35 |