Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data

Date
2007
Authors
Woods, B.J.
Clymer, B.D.
Kurc, T.
Heverhagen J.T.
Stevens, R.
Orsdemir, A.
Bulan O.
Knopp, M.V.
Advisor
Instructor
Source Title
Journal of Magnetic Resonance Imaging
Print ISSN
10531807
Electronic ISSN
Publisher
Volume
25
Issue
3
Pages
495 - 501
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
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.

Course
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Book Title
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
Citation
Published Version (Please cite this version)