Adaptive Local Contrast Enhancement Combined with 2D Discrete Wavelet Transform for Mammographic Mass Detection and Classification
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.
KeywordsBiomedical Image Processing X-Ray Local Image Enhancement Support Vector Machines
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- 1.Egan, R.: Breast Imaging: Diagnosis and Morphology of Breast Diseases. Saunders Co Ltd. (1988)Google Scholar
- 2.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)Google Scholar
- 4.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)Google Scholar
- 8.Raviraj, P., Sanavullah, M.: The modified 2D Haar wavelet transformation in image compression. Middle-East Journ. of Scient. Research 2 (2007)Google Scholar
- 9.Rejani, Y.I.A., Selvi, S.T.: Early detection of breast cancer using SVM classifier technique. CoRR, abs/0912.2314 (2009)Google Scholar
- 11.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)Google Scholar
- 12.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)CrossRefGoogle Scholar
- 13.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)Google Scholar