Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming

[+] Author Affiliations
Shijun Wang, Jianhua Yao, Ronald M. Summers

National Institutes of Health (USA)

Nicholas A. Petrick

U.S. Food and Drug Administration (USA)

Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602R (February 27, 2009); doi:10.1117/12.811219
Text Size: A A A
From Conference Volume 7260

  • Medical Imaging 2009: Computer-Aided Diagnosis
  • Nico Karssemeijer; Maryellen L. Giger
  • Lake Buena Vista, FL | February 07, 2009


Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible combination for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features, called histogram of curvature features, are rotation, translation and scale invariant and can be treated as complementing our existing feature set. Then in order to make full use of the traditional features (defined as group A) and the new features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to identify an optimized classification kernel based on the combined set of features. We did leave-one-patient-out test on a CTC dataset which contained scans from 50 patients (with 90 6-9mm polyp detections). Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per patient rate of 7, the sensitivity on 6-9mm polyps using the combined features improved from 0.78 (Group A) and 0.73 (Group B) to 0.82 (p<=0.01).

© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Shijun Wang ; Jianhua Yao ; Nicholas A. Petrick and Ronald M. Summers
"Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602R (February 27, 2009); doi:10.1117/12.811219; http://dx.doi.org/10.1117/12.811219

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