Name
Title | ||
|---|---|---|
Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography. |
Abstract | ||
|---|---|---|
The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10278-012-9547-6 | J. Digital Imaging |
Keywords | Field | DocType |
bp neural network,textural features,radiological features,solitary pulmonary nodules,feature selection | Nuclear medicine,Solitary pulmonary nodule,Co-occurrence matrix,Gray level,Computed tomography,Radiology,Area under the roc curve,Medicine,Radiological weapon | Journal |
Volume | Issue | ISSN |
26 | 4 | 1618-727X |
Citations | PageRank | References |
9 | 0.60 | 13 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haifeng Wu | 1 | 9 | 0.60 |
| Tao Sun | 2 | 168 | 16.47 |
| Jingjing Wang | 3 | 9 | 0.93 |
| Xia Li | 4 | 9 | 1.27 |
| Wei Wang | 5 | 7122 | 746.33 |
| Da Huo | 6 | 29 | 5.06 |
| Pingxin Lv | 7 | 25 | 1.46 |
| Wen He | 8 | 9 | 0.60 |
| Keyang Wang | 9 | 9 | 0.60 |
| Xiuhua Guo | 10 | 27 | 3.21 |