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L. Arbach, A. H. Stolpen, and J. M. Reinhardt. Classification of breast MRI lesions using a backpropagation neural network (BNN). In 2004 International Symposium on Biomedical Imaging, pp. 253-256, 2004.

Abstract: Breast cancer is the second leading cause of cancer deaths in women today. Mammography is currently the primary method of early detection. But research has shown that many cases missed by Mammography can be detected in breast MRI. Breast MRI is more difficult to interpret than mammography, because it generates much more data. Also, because it is a non-standard modality, there are fewer people qualified to use it for diagnosis. Our primary motivation is to increase the classification specificity for readers by using automatic classification system. We propose a method for finding significant regions in the data, and classifying them into malignant and benign. In this pilot study, our method first searches the breast tissue for contrast enhanced regions, then computes a difference image by subtracting the pre-contrast and post-contrast images. Two levels of thresholding are then applied. These thresholds are used to identify suspicious lesions and separate them from the background. After thresholding, 3D connected components analysis is used to label the enhanced lesions, and lesion shape based features are computed and used as inputs to the classifier. The last step is to use a backpropagation neural network (BNN) to classifying the labeled regions into benign and malignant, using the biopsy results as the gold standard. Our preliminary results show an area under the ROC curve for the testing stage equals to 0.913. This result illustrates the promise of using BNN as a physician?s assistant for breast MRI classification.

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Keywords: breast cad

Other publications by: L. Arbach, A. H. Stolpen, J. M. Reinhardt

Related journal papers:

L. Arbach, G. Fallouh, J. M. Reinhardt, and L. Bennett. Distinguishing between malignant and nonmalignant breast masses from mammograms using artificial intelligence techniques. Bassel Al-Assad J. for Engineering Science, vol. 16, pp. 103-121, 2002. (details)
L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo, and J. M. Reinhardt. Breast MRI Lesion Classification: Improved Performance of Human Readers with a Backpropagation Neural Network Computer-Aided Diagnosis (CAD) System. J. Magn. Res. Imaging, vol. 25, no. 1, pp. 85-95, 2007. (details)

Related conference papers:

L. Arbach, L. Bennett, J. M. Reinhardt, and G. Fallouh. Breast mass classification: Comparison between human readers and a back-propagation neural network. In M. Sonka and J. M. Fitzpatrick, eds., Proc. SPIE Conf. Medical Imaging, vol. 5032, pp. 810-818, San Diego, CA, 2003. (details)
L. Arbach, L. Bennett, J. M. Reinhardt, and G. Fallouh. Mammogram Breast Mass Classification with Backpropagation Neural Network. In IEEE Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1441-1444, 2003. (details)
M. Sonka, J. Tschirren, S. Ukil, X. Zhang, Y. Xu, J. M. Reinhardt, E. van Beek, G. McLennan, and E. A. Hoffman. Pulmonary CT image analysis and computer aided detection. In Biomedical Imaging: From Nano to Macro, ISBI 2007. 4th IEEE International Symposium on Biomedical Imaging, pp. 500-503, 2007. (details)

Related theses:

L. Arbach. Development of a computer-aided diagnostic system for breast MRI lesion classification. PhD thesis, The University of Iowa, Iowa City, IA, 2005. (details)