Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Abdominal lymphadenopathy detection using random forest

[+] Author Affiliations
Kevin M. Cherry, Shijun Wang, Evrim B. Turkbey, Ronald M. Summers

National Institutes of Health (United States)

Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351G (March 18, 2014); doi:10.1117/12.2043837
Text Size: A A A
From Conference Volume 9035

  • Medical Imaging 2014: Computer-Aided Diagnosis
  • Stephen Aylward; Lubomir M. Hadjiiski
  • San Diego, California, USA | February 15, 2014


We propose a new method for detecting abdominal lymphadenopathy by utilizing a random forest statistical classifier to create voxel-level lymph node predictions, i.e. initial detection of enlarged lymph nodes. The framework permits the combination of multiple statistical lymph node descriptors and appropriate feature selection in order to improve lesion detection beyond traditional enhancement filters. We show that Hessian blobness measurements alone are inadequate for detecting lymph nodes in the abdominal cavity. Of the features tested here, intensity proved to be the most important predictor for lymph node classification. For initial detection, candidate lesions were extracted from the 3D prediction map generated by random forest. Statistical features describing intensity distribution, shape, and texture were calculated from each enlarged lymph node candidate. In the last step, a support vector machine (SVM) was trained and tested based on the calculated features from candidates and labels determined by two experienced radiologists. The computer-aided detection (CAD) system was tested on a dataset containing 30 patients with 119 enlarged lymph nodes. Our method achieved an AUC of 0.762±0.022 and a sensitivity of 79.8% with 15 false positives suggesting it can aid radiologists in finding enlarged lymph nodes. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Kevin M. Cherry ; Shijun Wang ; Evrim B. Turkbey and Ronald M. Summers
" Abdominal lymphadenopathy detection using random forest ", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351G (March 18, 2014); doi:10.1117/12.2043837; http://dx.doi.org/10.1117/12.2043837

Access This Article
Sign In to Access Full Content
Please Wait... Processing your request... Please Wait.
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).



Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections


Buy this article ($18 for members, $25 for non-members).
Sign In