Non-parametric bootstrap ensembles for detection of tumor lesions

Mireya Diaz, J. Sunil Rao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We propose and assess a set of non-parametric ensembles, including bagging and boosting schemes, to recognize tumors in digital mammograms. Different approaches were examined as candidates for the two major components of the bagging ensembles, three spatial resampling schemes (residuals, centers and standardized centers), and four combination criteria (at least one, majority vote, top 25% models, and false discovery rate). A conversion to a classification problem prior to aggregation was employed for the boosting ensemble. The ensembles were compared at the lesion level against a single expert, and to a set of Markov Random Field (MRF) models in real images using three different criteria. The performance of the ensembles depended on its components, particularly the combination, with at least one and top 25% models offering a greater detection power independently of the type of lesion, and of the booststrapping scheme in a lesser degree. The ensembles were comparable in performance to MRFs in the unsupervised recognition of patterns exhibiting spatial structure.

Original languageEnglish (US)
Pages (from-to)2273-2283
Number of pages11
JournalPattern Recognition Letters
Issue number16
StatePublished - Dec 1 2007
Externally publishedYes


  • Ensembles
  • Image analysis
  • Markov Random Fields
  • Spatial correlation
  • Statistical pattern recognition
  • Unsupervised training

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering


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