Name
Abstract | ||
|---|---|---|
Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TBME.2014.2342653 | IEEE transactions on bio-medical engineering |
Keywords | Field | DocType |
mtbi lesions,mtbi rat model,discriminative voxel-level classifier,dynamic low-level context,multiple texture measures,dice score,dynamic context,injuries,biomedical mri,magnetic resonance imaging,feature extraction,image classification,magnetic resonance imaging (mri),visual features,posterior marginal edge,object detection,brain,mild traumatic brain injury,traumatic brain injury,low-level dynamic context features,contextual features,low contrast,image texture,low-level static context features,medical image processing | Convergence (routing),Spatial analysis,Computer vision,Object detection,Time point,Lesion,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Discriminative model,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
62 | 1 | 1558-2531 |
Citations | PageRank | References |
2 | 0.37 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anthony C. Bianchi | 1 | 8 | 1.56 |
| Bir Bhanu | 2 | 3356 | 380.19 |
| Andre Obenaus | 3 | 7 | 2.16 |