Name
Abstract | ||
|---|---|---|
Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MIS.2015.93 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Tumors,Feature extraction,Image segmentation,Pattern recognition,Image coding,Biomedical image processing,Deformable models | Computer vision,Pattern recognition,Segmentation,Computer science,Brain tumor,Image segmentation,Feature extraction,Artificial intelligence,Deformation (mechanics),Statistical classification,Magnetic resonance imaging,Feature data | Journal |
Volume | Issue | ISSN |
31 | 2 | 1541-1672 |
Citations | PageRank | References |
5 | 0.43 | 19 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shang-Ling Jui | 1 | 12 | 3.95 |
| Shichen Zhang | 2 | 5 | 0.43 |
| Weilun Xiong | 3 | 8 | 0.81 |
| Fangxiaoqi Yu | 4 | 5 | 0.43 |
| Mingjian Fu | 5 | 5 | 0.43 |
| Dongmei Wang | 6 | 8 | 2.17 |
| Aboul Ella Hassanien | 7 | 1610 | 192.72 |
| Kai Xiao | 8 | 14 | 6.10 |