Abstract
In this paper we present a new method for the automatic detection of microcalcifications combining morphological operations and artificial neural networks (ANN). The input chosen is a whole digitalized mammogram while the output of the algorithm is a new mammogram where microcalcifications (clustered or not) appear to be highlighted in white colour individually. Two new filters have been designed: one is based on mathematical morphology combined with other segmentation techniques and statistical methods, and the other one is made by training an ANN in order to be able to differentiate between those pixels belonging to a microcalcification and those being normal parenchymal patterns. Results for both algorithms are separately shown and how the combination of these two approaches improves the results.
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References
Goerge, S.K., Evans, J., Cohen, G.P.B., McMillan, J.H.: Characteristics of Breast Carcinomas Missed by Screening Radiologists. Radiology 204, 131–135 (1997)
Okada, M., Shridhar, M.: Extraction of User Entered Components from a Personal Bankcheck Using Morphological Subtraction. International Journal of Pattern Recognition and Artificial Intelligence 11(5), 699–715 (1997)
Betal, D., Roberts, N., Whitehouse, G.H.: Segmentation and Numerical Analysis of Microcalcifications on Mammograms Using Mathematical Morphology. The British Journal of Radiology 70, 903–917 (1997)
Robin, F., Privat, G., Renaudin, M.: Asynchronous Relaxation of Morphological Operators: A Joint Algorithm-Architecture Perspective. International Journal of Pattern Recognition and Artificial Intelligence 11(7), 1085–1094 (1997)
Shih-Chung, B.L., Heang-Ping, C., Jyh-Shyan, L., et al.: Artificial Convolution Neural Network for Medical Image Pattern Recognition. Neural Networks 8(7/8), 1201–1214 (1995)
Boone, J.M., Gross, G.W., Greco-Hunt, V.: Neural Networks in Radiologic Diagnosis: I. Introduction and Illustration. Ivest. Radiol. 25, 1012–1016 (1990)
Asada, N., Doi, K., MacMahon, H., Montner, S.M., et al.: Neural Network Approach for Differential Diagnosis of Interstitial Lung Diseases. In: Proc. SPIE Med. Imag., vol. 1233, pp. 45–50 (1990)
Wu, Y., Giger, M.L., Nishikawa, M.: Computerized Detection of Clustered Microcalcifications in Digital Mammograms: Applications of Artificial Neural Networks. Medical Physics 19(3), 555–560 (1992)
Crespo, J.: Morphological Connected Filters and Intra-Region Smoothing for Image Segmentation. PhD Thesis. Georgia Institute of Technology (1993)
Maren, A., Harston, C., Pap, R.: Handbook of Neural Computing Applications. Academic Press, London (1990)
Metz, C.E.: ROC Methodology in Radiologic Imaging. Investigative Radiology 21, 720–733 (1886)
Peña Sánchez de Rivera, D.: Estadistica Modelos y Métodos. Alianza Universidad Textos, 2nd edn., Madrid (1991)
Gonzalo, R.C., Woods, R.E.: Digital Image Processing. Addison Wesley, Reading (1993)
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© 1999 Springer-Verlag Berlin Heidelberg
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Manrique, D., Ríos, J., Vilarrasa, A. (1999). A Combined Neural Network and Mathematical Morphology Method for Computerized Detection of Microcalcifications. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_12
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DOI: https://doi.org/10.1007/978-3-540-48765-4_12
Publisher Name: Springer, Berlin, Heidelberg
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