Abstract
This paper presents an automated knowledge-based vision system for mass detection and classification in X-Ray mammograms. The system developed herein is based on several processing steps, which aim first at identifying the various regions of the mammogram such as breast, markers, artifacts and background area and then to analyze the identified areas by applying a contrast improvement method for highlighting the pixels of the candidate masses. The detection of such candidate masses is then done by applying locally a 2D Haar Wavelet transform, whereas the mass classification (in benign and malignant ones) is performed by means of a support vector machine whose features are the spatial moments extracted from the identified masses. The system was tested on the public database MIAS achieving very promising results in terms both of accuracy and of sensitivity.
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
Egan, R.: Breast Imaging: Diagnosis and Morphology of Breast Diseases. Saunders Co Ltd. (1988)
Giordano, D., Spampinato, C., Scarciofalo, G., Leonardi, R.: EMROI extraction and classification by adaptive thresholding and DoG filtering for automated skeletal bone age analysis. In: Proc. of the 29th EMBC Conference, pp. 6551–6556 (2007)
Giordano, D., Spampinato, C., Scarciofalo, G., Leonardi, R.: An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial/metaphysial bones. IEEE Transactions on Instrumentation and Measurement 59(10), 2539–2553 (2010)
Hadhou, M., Amin, M., Dabbour, W.: Detection of breast cancer tumor algorithm using mathematical morphology and wavelet analysis. In: Proc. of GVIP 2005, pp. 208–213 (2005)
Kecman, V.: Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models. MIT Press, Cambridge (2001)
Kom, G., Tiedeu, A., Kom, M.: Automated detection of masses in mammograms by local adaptive thresholding. Comput. Biol. Med. 37, 37–48 (2007)
Oliver, A., Freixenet, J., Marti, J., Perez, E., Pont, J., Denton, E.R., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14, 87–110 (2010)
Raviraj, P., Sanavullah, M.: The modified 2D Haar wavelet transformation in image compression. Middle-East Journ. of Scient. Research 2 (2007)
Rejani, Y.I.A., Selvi, S.T.: Early detection of breast cancer using SVM classifier technique. CoRR, abs/0912.2314 (2009)
Rojas Dominguez, A., Nandi, A.K.: Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput. Med. Imaging Graph 32, 304–315 (2008)
Sampat, M., Markey, M., Bovik, A.: Computer-aided detection and diagnosys in mammography. In: Handbook of Image and Video Processing, 2nd edn., pp. 1195–1217 (2005)
Shi, J., Sahiner, B., Chan, H.P., Ge, J., Hadjiiski, L., Helvie, M.A., Nees, A., Wu, Y.T., Wei, J., Zhou, C., Zhang, Y., Cui, J.: Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med. Phys. 35, 280–290 (2008)
Suckling, J., Parker, D., Dance, S., Astely, I., Hutt, I., Boggis, C.: The mammographic images analysis society digital mammogram database. Exerpta Medical International Congress Series, pp. 375–378 (1994)
Suliga, M., Deklerck, R., Nyssen, E.: Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Comput. Med. Imaging Graph 32, 502–512 (2008)
Timp, S., Karssemeijer, N.: A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med. Phys. 31, 958–971 (2004)
Wei, J., Sahiner, B., Hadjiiski, L.M., Chan, H.P., Petrick, N., Helvie, M.A., Roubidoux, M.A., Ge, J., Zhou, C.: Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 32, 2827–2838 (2005)
Zhang, L., Sankar, R., Qian, W.: Advances in micro-calcification clusters detection in mammography. Comput. Biol. Med. 32, 515–528 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giordano, D., Kavasidis, I., Spampinato, C. (2011). Adaptive Local Contrast Enhancement Combined with 2D Discrete Wavelet Transform for Mammographic Mass Detection and Classification. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-21984-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21983-2
Online ISBN: 978-3-642-21984-9
eBook Packages: Computer ScienceComputer Science (R0)