Hybrid committee classifier for a computerized colonic polyp detection system

Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features (numbers varied from 10-20) were selected for 11 NN classifiers which were again combined to form a NN committee classifier. Finally, a hybrid committee classifier was defined by combining the outputs of both the SVM and NN committees. The method was tested on CTC scans (supine and prone views) of 29 patients, in terms of the partial area under a free response receiving operation characteristic (FROC) curve (AUC). Our results showed that the hybrid committee classifier performed the best for the prone scans and was comparable to other classifiers for the supine scans.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2006
Subtitle of host publicationImage Processing
StatePublished - Jun 22 2006
EventMedical Imaging 2006: Image Processing - San Diego, CA, United States
Duration: Feb 13 2006Feb 16 2006

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6144 III
ISSN (Print)1605-7422


OtherMedical Imaging 2006: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Classifier Committee
  • Computer-Aided Detection
  • Neural Network
  • Pattern Recognition
  • Statistical Methods
  • Support Vector Machine

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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