Name
Title | ||
|---|---|---|
Femoral cartilage segmentation in knee MRI scans using two stage voxel classification. |
Abstract | ||
|---|---|---|
Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines (SVMs). Here we apply it to femoral cartilage segmentation. We describe the similarities and differences between segmenting these two knee cartilages. For further speeding up batch SVM training, we propose loosening the stopping condition in the quadratic program solver before considering moving on to other approximation techniques such as online SVMs. The two-stage approach reached a higher accuracy in comparison to the one-stage state-of-the-art method. It also achieved better inter-scan segmentation reproducibility when compared to a radiologist as well as the current state-of-the-art method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610787 | EMBC |
Keywords | Field | DocType |
classifier scale,approximation techniques,training samples,quadratic program solver,quadratic programming,large-scale training data,radiologist,online support vector machine,image segmentation,femoral cartilage segmentation,one-stage state-of-the-art method,nearest neighbor classifier,biomedical mri,magnetic resonance imaging,osteoarthritis,svm,image classification,support vector machine,interscan segmentation reproducibility,nearest neighbor classification,two-stage voxel classification,biological tissues,femoral cartilage,class population imbalance,support vector machines,knee mri scans,medical image processing,training data,sociology,statistics | Voxel,Population,Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Support vector machine,Image segmentation,Artificial intelligence,Contextual image classification,Classifier (linguistics) | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adhish Prasoon | 1 | 113 | 6.43 |
| Christian Igel | 2 | 49 | 2.82 |
| Marco Loog | 3 | 1796 | 154.31 |
| François Lauze | 4 | 306 | 29.69 |
| Erik B. Dam | 5 | 87 | 10.97 |
| Mads Nielsen | 6 | 1197 | 156.23 |