Abstract
A technique is proposed to classify regions of interests (ROIs) of digitized mammograms into mass and non-mass regions using texture features and artificial neural network (ANN). Fifty ROIs were extracted from the MIAS MiniMammographic Database, with 25 ROIs containing masses and 25 ROIs containing normal breast tissue only. Twelve texture features were derived from the gray level co-occurrence matrix (GLCM) of each region. The sequential forward selection technique was used to select four significant features from the twelve features. These significant features were used in the ANN to classify the ROI into either mass or non-mass region. By using leave-one-out method on the 50 images using the four significant features, classification accuracy of 86% was achieved for ANN. The test result using the four significant features is better than the full set of twelve features. The proposed method is compared with some existing works and promising results are obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Constantinidis, A.S., Fairhurst, M.C., Deravi, F., Hanson, M., Wells, C.P., Chapman-Jones, C.: Evaluating Classification Strategies for Detection of Circumscribed Masses in Digital Mammograms. In: Proceedings of 7th Int’l Conference on Image Processing and Its Applications, pp. 435–439 (1999)
Bovis, K., Singh, S., Fieldsend, J., Pinder, C.: Identification of Masses in Digital Mammograms with MLP and RBF Nets. In: Proceedings of the IEEE-INNS-ENNS International joint Conference in Neural Networks, pp. 342–347 (2000)
Tang, J., Rangayyan, R.M., Xu, J., Naqa, I.E., Yang, Y.: Computer-Aided Detection and Diagnosis of Breast Cnacer with Mammography: Recent Advances. IEEE Trans. on Information Technology in Biomedicine 13(2), 236–251 (2009)
Cheng, H., Cai, X.P., Chen, X.W., Hu, X.L., Lou, X.L.: Computer Aided Detection and Classification of Microcalcifications in Mammograms: a Survey. Pattern Recognition 36, 2967–2991 (2003)
Cheng, H., Shi, X., Min, R., Hu, L., Cai, X., Du, H.: Approaches for Automated Detection & Classification of Masses in Mammograms. Pattern Recognition 39, 664–668 (2006)
Whitney, A.W.: A Direct Method of Nonparametric Measurement Selection. IEEE Trans. on Computers C-20(9), 1100–1103 (1971)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Neural Classification of Abnormal Tissue in Digital mammography Using Statistical Features of the Texture. In: Proc. of the 6th IEEE Int’l Conf. on Electronics, Circuits & Systems, vol. 1, pp. 117–120 (1999)
Petrosian, A., Chan, H., Helvie, M., Goodsitt, M., Adler, D.: Computer-aided Diagnosis in Mammography: Classification of Mass and Normal Tissue by Texture Analysis. Physics in Medicine and Biology 39(12), 2273–2288 (1994)
Chan, H., Wei, D., Helvie, M., Sahiner, B., Adler, D., Goodsitt, M., Petrick, N.: Computer-aided Classification of Mammographic Masses and Normal Tissue: Linear Discriminant Analysis in Texture Feature Space. Physics in Med. & Biol. 40, 857–876 (1995)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture Features for Image Classification. IEEE Trans. Syst. Man Cybernet. 3(6), 610–621 (1973)
Masters, T.: Practical Neural Network Recipes in C++. UK edn. Academic Press (1993)
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., Savage, J.: The Mammographic Image Analysis Society digital mammogram database. In: Proceedings of the 2nd International Workshop on Digital Mammography, vol. 1069, pp. 375–378 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wong, M.T., He, X., Nguyen, H., Yeh, WC. (2012). Mass Classification in Digitized Mammograms Using Texture Features and Artificial Neural Network. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-34500-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34499-2
Online ISBN: 978-3-642-34500-5
eBook Packages: Computer ScienceComputer Science (R0)