Abstract
Among breast abnormalities, spiculated lesions are one of the most difficult type of tumor to detect. In this paper, we apply a feature extraction method to generate four feature images for a single mammogram, and then partition every feature image into a series of small square blocks. The four average feature values of each block are considered as an instance describing the block. Finally we use an artificial neural network ensemble method to detect the spiculated lesions. Experiments show that the accuracy of this method is well on digital mammograms.
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References
American Cancer Society: Cancer Facts And Figures 2003: Technical Report, American Cancer Society, Atlanta, GA (2003)
Christoyianni, I., Koutras, A., Dermatas, E., Kokkinakis, G.: Computer Aided Diagnosis of Breast Cancer in Digitized Mammograms. Computerized Medical Imaging and Graphics 26, 309–319 (2002)
Kophans, D.B.: Breast Imaging, 2nd edn. Lippincott Williams (1998)
Liu, S., Babbs, C.F., Delp, E.J.: Multiresolution Detection of Spiculated Lesions in Digital Mammograms. IEEE Transactions on Image Processing 10, 874–884 (2001)
Hornik, K.M., Stinchcombe, M., White, H.: Multilayer Feedforward Networks Are Universal Approximators. Neural Networks 2, 359–366 (1989)
Sollich, P., Krogh, A.: Learning with Ensembles: How Over-Fitting Can Be Useful. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems 8, pp. 190–196. MIT Press, Cambridge (1996)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems 7, pp. 231–238. MIT Press, Cambridge (1995)
Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 5, 197–227 (1990)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Zhou, Z.-H., Jiang, Y., Yang, Y.-B., Chen, S.-F.: Lung Cancer Cell Identification Based on Artificial Neural Network Ensembles. Artificial Intelligence in Medicine 24, 25–36 (2002)
Zhou, Z.H., Jiang, Y.: Medical Diagnosis with C4.5 Rule Preceded by Artificial Neural Network Ensemble. IEEE Transactions on Information Technology in Biomedicine 7, 37–42 (2003)
American College of Radiology: Illustrated Breast Imaging Reporting and Data System, 3rd edn., Reston(VA): American College of Radiology (1998)
Kegelmeyer Jr., W.P., Prundeda, J.M., Bourland, P.D., Hillis, A., Riggs, M.W., Nipper, M.L.: Computer-aided Mammographic Screening for Spiculated Lesions. Radiology 191, 331–336 (1994)
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, N., Zhou, H., Ling, J., Zhou, Z. (2005). Spiculated Lesion Detection in Digital Mammogram Based on Artificial Neural Network Ensemble. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_125
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DOI: https://doi.org/10.1007/11427469_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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