title: Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms.
abstract: BACKGROUND
  Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance.
  
  
  OBJECTIVE
  We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms.
  
  
  METHODS
  Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module.
  
  
  RESULTS
  By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi.
  
  
  CONCLUSIONS
  By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.
authors.affiliation: University of Kragujevac, Kragujevac, Serbia.|Research and Development Centre for Bioengineering, Kragujevac, Serbia.
authors.forename: Milos
authors.initials: M
authors.lastname: Radovic
authors.affiliation: University of Kragujevac, Kragujevac, Serbia.
authors.forename: Marina
authors.initials: M
authors.lastname: Milosevic
authors.affiliation: Clinical Center Kragujevac, Kragujevac, Serbia.
authors.forename: Srdjan
authors.initials: S
authors.lastname: Ninkovic
authors.affiliation: University of Kragujevac, Kragujevac, Serbia.
authors.forename: Nenad
authors.initials: N
authors.lastname: Filipovic
authors.affiliation: University of Kragujevac, Kragujevac, Serbia.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Peulic
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results
keywords: Breast cancer; computer-assisted image processing; data mining; mammography; optimization