title: Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data.
abstract: OBJECTIVE
  To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images.
  
  
  METHODS
  4D texture analysis was performed on DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2).
  
  
  RESULTS
  The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with alpha = 0.05. The results show that an area under the ROC curve (A(z)) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively.
  
  
  CONCLUSIONS
  This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted.
authors.affiliation: Department of Electrical and Computer Engineering, Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Brent J
authors.initials: BJ
authors.lastname: Woods
authors.forename: Bradley D
authors.initials: BD
authors.lastname: Clymer
authors.forename: Tahsin
authors.initials: T
authors.lastname: Kurc
authors.forename: Johannes T
authors.initials: JT
authors.lastname: Heverhagen
authors.forename: Robert
authors.initials: R
authors.lastname: Stevens
authors.forename: Adem
authors.initials: A
authors.lastname: Orsdemir
authors.forename: Orhan
authors.initials: O
authors.lastname: Bulan
authors.forename: Michael V
authors.initials: MV
authors.lastname: Knopp
mesh_terms: D001706:Biopsy / Q000379:methods; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis* / Q000473:pathology; D003287:Contrast Media / Q000008:administration & dosage*; D003937:Diagnosis, Differential; D005260:Female; D005348:Fibrocystic Breast Disease / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008536:Meglumine / Q000031:analogs & derivatives; D016571:Neural Networks, Computer; D015588:Observer Variation; D009942:Organometallic Compounds; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine