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Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data
Author(s) -
Woods Brent J.,
Clymer Bradley D.,
Kurc Tahsin,
Heverhagen Johannes T.,
Stevens Robert,
Orsdemir Adem,
Bulan Orhan,
Knopp Michael V.
Publication year - 2007
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.20837
Subject(s) - segmentation , receiver operating characteristic , artificial intelligence , pattern recognition (psychology) , voxel , classifier (uml) , computer science , magnetic resonance imaging , computer aided diagnosis , radiology , medicine , machine learning
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 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 α = 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. 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. J. Magn. Reson. Imaging 2007. © 2007 Wiley‐Liss, Inc.