Abstract
We present an unsupervised method for fully automatic detection of regions of interest containing fibroglandular tissue in digital screening mammography. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. The mammogram tissue textures are locally represented by four causal monospectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous mammogram segments is reached. The performance of the presented method is extensively tested on the Digital Database for Screening Mammography (DDSM) from the University of South Florida as well as on the Prague Texture Segmentation Benchmark using the commonest segmentation criteria and where it compares favourably with several alternative texture segmentation methods.
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
Tweed, T., Miguet, S.: Automatic detection of regions of interest in mammographies based on a combined analysis of texture and histogram. ICPR 2, 448–452 (2002)
Qi, H., Diakides, N.A.: Thermal infrared imaging in early breast cancer detection - a survey of recent research. In: 25th Annual Int. Conference of the IEEE EMBS, pp. 448–452. IEEE Computer Society Press, Los Alamitos (2003)
Kashyap, R.: Image models. In: Young, T.Y. (ed.) Handbook of Pattern Recognition and Image Processing, Academic Press, New York (1986)
Reed, T.R., du Buf, J.M.H.: A review of recent texture segmentation and feature extraction techniques. CVGIP–Image Understanding 57, 359–372 (1993)
Haindl, M.: Texture synthesis. CWI Quarterly 4, 305–331 (1991)
Panjwani, D., Healey, G.: Markov random field models for unsupervised segmentation of textured color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 939–954 (1995)
Manjunath, B., Chellapa, R.: Unsupervised texture segmentation using markov random field models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 478–482 (1991)
Andrey, P., Tarroux, P.: Unsupervised segmentation of markov random field modeled textured images using selectionist relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 252–262 (1998)
Haindl, M.: Texture segmentation using recursive markov random field parameter estimation. In: Bjarne, K., Peter, J., (eds.) Proceedings of the 11th Scandinavian Conference on Image Analysis, Lyngby, Denmark, Pattern Recognition Society of Denmark, pp. 771–776 (1999)
Haindl, M., Mikeš, S.: Model-based texture segmentation. In: Campilho, A., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 306–313. Springer, Heidelberg (2004)
Haindl, M., Šimberová, S.: A Multispectral Image Line Reconstruction Method. In: Theory & Applications of Image Analysis, pp. 306–315. World Scientific Publishing Co. Singapore (1992)
Kittler, J., Hojjatoleslami, A., Windeatt, T.: Weighting factors in multiple expert fusion. In: Proc. BMVC, BMVA, BMVA, pp. 41–50 (1997)
Haindl, M., Mikeš, S.: Unsupervised texture segmentation using multiple segmenters strategy. In: LNCS, vol. 4472, 2007 (accepted)
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Proc. of the 5th Int. Workshop on Digital Mammography, Medical Physics Publishing (2000)
Deng, Y., Manjunath, B.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 800–810 (2001)
Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: Blobworld: A system for region-based image indexing and retrieval. In: Third International Conference on Visual Information Systems, Springer, Heidelberg (1999)
Christoudias, C., Georgescu, B., Meer, P.: Synergism in low level vision. In: Kasturi, R., Laurendeau, D., Suen, C. (eds.) Proceedings of the 16th International Conference on Pattern Recognition, vol. 4, pp. 150–155. IEEE Computer Society, Los Alamitos (2002)
Mikeš, S., Haindl, M.: Prague texture segmentation data generator and benchmark. ERCIM News, pp. 67–68 (2006), http://mosaic.utia.cas.cz
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haindl, M., Mikeš, S., Scarpa, G. (2007). Unsupervised Detection of Mammogram Regions of Interest. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-74829-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74828-1
Online ISBN: 978-3-540-74829-8
eBook Packages: Computer ScienceComputer Science (R0)