Name
Title | ||
|---|---|---|
Automated Brain Tumor Segmentation Using Kernel Dictionary Learning And Superpixel-Level Features |
Abstract | ||
|---|---|---|
Brain tumor segmentation, an essential but challenging task, has long attracted much attention from the medical imaging community. Recently, successful applications of sparse coding and dictionary learning has emerged in various vision problems including image segmentation. In this paper, a superpixel-based framework for automated brain tumor segmentation is introduced. The kernel trick is adopted in dictionary learning to transform superpixel-level features to a high-dimensional feature space where their nonlinear similarities are considered to generate discriminative sparse codes. A graph is constructed from the approximation errors given by dictionaries modeling different brain tumor structures so that superpixels belonging to particular tumor regions can be efficiently identified. The proposed framework is evaluated on brain magnetic resonance images of high-grade glioma (HGG) patients provided by the multi-modal Brain Tumor Segmentation (BRATS) Benchmark. Results show that the proposed framework achieves competitive performance when compared with the state-of-the-art methods. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Tumor segmentation, kernel methods, superpixels, sparse coding, dictionary learning, graph-cuts |
Field | DocType | ISSN |
Feature vector,Scale-space segmentation,Pattern recognition,Computer science,Neural coding,Segmentation-based object categorization,Feature extraction,Image segmentation,Artificial intelligence,Kernel method,Discriminative model,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Chen | 1 | 0 | 0.68 |
| Binh P. Nguyen | 2 | 80 | 10.61 |
| Chee-Kong Chui | 3 | 245 | 38.34 |
| Sim Heng Ong | 4 | 426 | 44.63 |