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Breast cancer detection in gadolinium‐enhanced MR images by static region descriptors and neural networks
Author(s) -
Tzacheva Angelina A.,
Najarian Kayvan,
Brockway John P.
Publication year - 2003
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.10259
Subject(s) - gadolinium , breast cancer , magnetic resonance imaging , cancer , medicine , artificial neural network , radiology , computer science , artificial intelligence , chemistry , organic chemistry
Purpose To automate the diagnosis of malignancy by classifying breast tissues as negative or positive for malignancy in gadolinium‐enhanced dynamic magnetic resonance (MR) images, using static region descriptors and a neural network classifier. Materials and Methods We propose a novel approach whereby the classifier evaluates a number of parameters that identify important tumor characteristics, as obtained by digital image processing techniques. These parameters include static signal intensity (SI) after contrast enhancement, mass margin descriptors, evaluation of mass shape by calculation of eccentricity, mass size, and mass granularity by texture analysis. Datasets for 14 patients were obtained by use of the 1.5T PMRTOW Clinical Imager. Results Statistical performance evaluation of the neural networks indicated 90%–100% sensitivity, 91%–100% specificity, and 91%–100% accuracy. Conclusion Although this work is preliminary, it may reduce overall health‐care time and costs, and enable higher accuracy in automated breast cancer detection systems. J. Magn. Reson. Imaging 2003;17:337–342. © 2003 Wiley‐Liss, Inc.