Name
Abstract | ||
|---|---|---|
Traumatic and degenerative shoulder pathologies of which treatment strategy and success depends on correct diagnosis are more commonly encountered at the present time. The aim of this study is to help clinicians by using computer based decision support systems to diagnose correctly the degenerative and traumatic conditions of shoulder from MR images which is not an easy task in practice. Image patches containing the humeral head were generated from PI) weighted MR images of patients presented with pain by automatic segmentation with the region-based active contour method. Discriminative features required to classify humeral heads as normal, edematous and Hill-Sachs deformity were extracted by bag of features method and classified with decision support machines with a success rate of 92%. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | Bag-of-Features,PD weighted MR images,classification |
Field | DocType | ISSN |
Active contour model,Computer vision,Pattern recognition,Computer science,Segmentation,Bag of features,Decision support system,Image segmentation,Feature extraction,Artificial intelligence,Discriminative model,Deformity | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aysun Sezer | 1 | 5 | 3.51 |
| Hasan Basri Sezer | 2 | 5 | 2.50 |