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      • Department of Electrical and Electronics Engineering
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      Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data

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      Author(s)
      Woods, B.J.
      Clymer, B.D.
      Kurc, T.
      Heverhagen J.T.
      Stevens, R.
      Orsdemir, A.
      Bulan O.
      Knopp, M.V.
      Date
      2007
      Source Title
      Journal of Magnetic Resonance Imaging
      Print ISSN
      10531807
      Volume
      25
      Issue
      3
      Pages
      495 - 501
      Language
      English
      Type
      Article
      Item Usage Stats
      176
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      176
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      Abstract
      Purpose: 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. Materials and Methods: 4D texture analysis was performedon 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 α = 0.05. The results show that an area under the ROC curve (Az) 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. Conclusion: 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. © 2007 Wiley-Liss, Inc.
      Keywords
      Breast
      Cancer
      DCE-MRI
      Neural network
      Texture analysis
      adult
      area under the curve
      article
      artificial neural network
      benign tumor
      breast cancer
      breast carcinoma
      clinical article
      contrast enhancement
      diagnostic value
      differential diagnosis
      female
      fibrocystic breast disease
      human
      malignant neoplastic disease
      nuclear magnetic resonance imaging
      priority journal
      radiologist
      roc curve
      sensitivity and specificity
      Biopsy
      Breast
      Breast Neoplasms
      Carcinoma, Ductal, Breast
      Contrast Media
      Diagnosis, Differential
      Female
      Fibrocystic Breast Disease
      Humans
      Image Enhancement
      Image Interpretation, Computer-Assisted
      Image Processing, Computer-Assisted
      Magnetic Resonance Imaging
      Meglumine
      Neural Networks (Computer)
      Observer Variation
      Organometallic Compounds
      Retrospective Studies
      ROC Curve
      Sensitivity and Specificity
      Permalink
      http://hdl.handle.net/11693/23528
      Published Version (Please cite this version)
      http://dx.doi.org/10.1002/jmri.20837
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      • Department of Electrical and Electronics Engineering 3702
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