title: Improved discrimination of breast lesions using selective sampling of segmented MR images.
abstract: OBJECTIVE
  The aim of this work is to examine if the specificity of differentiation between malignant and benign tumours can be improved by retrospectively examining lesion-extracted distributions. A semi-automated method for selecting a region-of-interest (ROI) is described. A new histogram segmentation approach for sampling pharmacokinetic breast maps of transfer uptake is defined in order to assign classification variables for the lesion.
  
  
  METHODS
  Fifty exchange rate parameter maps were extracted from 49 subjects and retrospectively analysed. Distributions obtained from semi-automatically delineated ROIs were subdivided into ten overlapping segments. Parameters were extracted from each segment which effectively presents a new pixel intensity sampling strategy. Mann-Whitney non-parametric tests and ROC curves were generated.
  
  
  RESULTS
  Correlation exists between mean parameter values drawn from semi-automatically or manually drawn ROIs. However, the former yield higher specificity values as applied to this subset of enhancing benign lesions. Segmenting the exchange rate parameter histogram allows the identification of which part of the distribution correlates most with tumour type. Significant improvement in specificity is obtained when using half the pixels within the ROI.
  
  
  CONCLUSIONS
  Improved specificity values are obtained by a new method of selecting the differentiation parameters which relies on intensity rather than spatial segmentation. Only half the pixels available within the ROI contributed to the measured classification parameters.
authors.affiliation: Department of Physics, UAE University, Al-Ain, P.O. Box 17551, United Arab Emirates. B.Issa@uaeu.ac.ae
authors.forename: Bashar
authors.initials: B
authors.lastname: Issa
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity