Abstract
Presence of mass in breast tissues is highly indicative of breast cancer. The research work investigates the significance of neural-association of mass type of breast abnormality patterns for benign and malignant class characterization using auto-associator neural network and original features. The characterized patterns are finally classified into benign and malignant classes using a classifier neural network. Grey-level based statistical features, BI-RADS features, patient age feature and subtlety value feature have been used in proposed research work. The proposed research technique attained a 94% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tourassi, G.D.: Current Status of Computerized Decision Support Systems in Mammography. Studies in Fuzziness and Soft Computing 184, 173–208 (2005)
Bassett, L.W.: 30F Imaging the Breast: Section 9 Principles of Imaging. In: Robert, M.D., Bast Jr., C., et al. (eds.) Cancer Medicine, 5th edn., pp. 420–427. The American Cancer Society and BC Decker, Inc (2000)
Sampat, M., Markey, M., Bovik, A.: Computer-Aided Detection and Diagnosis in Mammography. In: Handbook of Image and Video Processing, pp. 1195–1217 (2005)
Bird, R.E., Wallace, T.W., Yankaskas, B.C.: Analysis of Cancers Missed at Screening Mammography. Radiology 184(3), 613–617 (1992)
Birdwell, R.L., Ikeda, D.M., O’Shaughnessy, K.F., Sickles, E.A.: Mammographic Characteristics of 115 Missed Cancers Later Detected with Screening Mammography and the Potential Utility of Computer-aided Detection. Radiology 219(1), 192–202 (2001)
Baker, J.A., Kornguth, P.J., Lo, J.Y., Williford, M.E., Floyd Jr., C.E.: Breast Cancer: Prediction with Artificial Neural Network based on BI-RADS Standardized Lexicon. Radiology 196(3), 817–822 (1995)
Wei, D., Chan, H.-P., Petrick, N., Sahiner, B., Helvie, M.A., Adler, D.D., Goodsitt, M.M.: False-Positive Reduction Technique for Detection of Masses on Digital Mammograms: Global and Local Multiresolution Texture Analysis. Medical Physics 24(6), 903–914 (1997)
Sahiner, B., Chan, H.-P., Petrick, N., Helvie, M.A., Goodsitt, M.M.: Computerized Characterization of Masses on Mammograms: The Rubber Band Straightening Transform and Texture Analysis. Medical Physics 25(4), 516–526 (1998)
Bovis, K., Singh, S.: Detection of Masses in Mammograms using Texture Features. In: Proc. of the 15th International Conference on Pattern Recognition (2000)
Bovis, K., Singh, S., Fieldsend, J., Pinder, C.: Identification of Masses in Digital Mammograms with MLP and RBF Nets. In: Proc. of the IEEE International Joint Conference on Neural Networks (IEEE-INNS-ENNS 2000) (2000)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Fast Detection of Masses in Computer-Aided Mammography. Signal Processing Magazine 17(1), 54–64 (2000)
Edwards, D.C., Lan, L., Metz, C.E., Giger, M.L., Nishikawa, R.M.: Estimating Three-Class Ideal Observer Decision Variables for Computerized Detection and Classification of Mammographic Mass Lesions. Medical Physics 31(1), 81–90 (2004)
Kupinski, M.A., Lan, L., Metz, C.E., Giger, M.L., Nishikawa, R.M.: Ideal Observer Approximation using Bayesian Classification Neural Networks. IEEE Transactions on Medical Imaging 20(9), 886–899 (2001)
Wu, Y., He, J., Man, Y., Arribas, J.I.: Neural Network Fusion Strategies for Identifying Breast Masses. In: Proc. of the IEEE International Joint Conference on Neural Networks (IEEE-IJCNN 2004) (2004)
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer Jr., P.: The Digital Database for Screening Mammography. In: Proc. of the Digital Mammography: IWDM 2000, 5th International Workshop on Digital Mammography, Medical Physics Publishing (2001)
Verma, B., Zakos, J.: A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Transactions on Information Technology in Biomedicine 5(1), 46–54 (2001)
Lo, J.Y., Gavrielides, M.A., Markey, M.K., Jesneck, J.L.: Computer-Aided Classification of Breast Microcalcification Clusters: Merging of Features from Image Processing and Radiologists. In: Proc. of the SPIE Medical Imaging 2003, Image Processing (2003)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
Iversen, A., Taylor, N.K., Brown, K.E.: Classification and Verification through the Combination of the Multi-Layer Perceptron and Auto-Association Neural Networks. In: Proc. of the IEEE International Joint Conference on Neural Networks (IEEE-IJCNN 2005) (2005)
Panchal, R., Verma, B.: A Fusion of Neural Network Based Auto-associator and Classifier for the Classification of Microcalcification Patterns. In: Proc. of the 11th International Conference on Neural Information Processing (ICONIP 2004) (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Panchal, R., Verma, B. (2006). Characterization of Breast Abnormality Patterns in Digital Mammograms Using Auto-associator Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_15
Download citation
DOI: https://doi.org/10.1007/11893295_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)