Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data
Author(s)
Date
2007Source Title
Journal of Magnetic Resonance Imaging
Print ISSN
10531807
Volume
25
Issue
3
Pages
495 - 501
Language
English
Type
ArticleItem 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
BreastCancer
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/11693/23528Published Version (Please cite this version)
http://dx.doi.org/10.1002/jmri.20837Collections
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