Journal of Digital Imaging

, Volume 26, Issue 4, pp 797–802 | Cite as

Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography

  • Haifeng Wu
  • Tao Sun
  • Jingjing Wang
  • Xia Li
  • Wei Wang
  • Da Huo
  • Pingxin Lv
  • Wen He
  • Keyang Wang
  • Xiuhua Guo


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.


Radiological features Textural features Feature selection Solitary pulmonary nodules BP neural network 



The program of Natural Science Fund of China (Serial Number: 81172772 and 30972550); the program of Natural Science Fund of Beijing (Serial Number: 4112015); the Program of Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (Serail Number: PHR201007112).


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Copyright information

© Society for Imaging Informatics in Medicine 2013

Authors and Affiliations

  • Haifeng Wu
    • 1
  • Tao Sun
    • 1
  • Jingjing Wang
    • 1
  • Xia Li
    • 1
    • 2
  • Wei Wang
    • 1
  • Da Huo
    • 1
  • Pingxin Lv
    • 3
  • Wen He
    • 4
  • Keyang Wang
    • 4
  • Xiuhua Guo
    • 2
    • 5
  1. 1.School of Public Health and Family MedicineCapital Medical UniversityBeijingChina
  2. 2.Beijing Municipal Key Laboratory of Clinical EpidemiologyBeijingChina
  3. 3.Department of Radiology, Beijing Chest HospitalCapital Medical UniversityBeijingChina
  4. 4.Department of Radiology, Friendship HospitalCapital Medical UniversityBeijingChina
  5. 5.Department of Epidemiology and Health Statistics, School of Public Health and Family MedicineCapital Medical UniversityBeijingChina

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