Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data
Journal of Magnetic Resonance Imaging
495 - 501
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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.
area under the curve
artificial neural network
fibrocystic breast disease
malignant neoplastic disease
nuclear magnetic resonance imaging
sensitivity and specificity
Carcinoma, Ductal, Breast
Fibrocystic Breast Disease
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Neural Networks (Computer)
Sensitivity and Specificity
Published Version (Please cite this version)http://dx.doi.org/10.1002/jmri.20837
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