Abstract
To establish a practical CAD (Computer-Aided Diagnosis) system to facilitate the diagnosis of microcalcifications, we propose a filter-based technique to detect microcalcifications. Via examination of an existing optimal filter-based technique, it is found that its performance on highlighting the energy of mammograms is seriously affected by artefacts and the background of breast. As a result, four methods in pre and post-processing are described in this paper to improve the optimal filtering, leading to an adaptive selection of thresholds for input mammograms. These methods have been tested by using 30 mammograms (with 25 microcalcifications) from the MIAS database and 23 mammograms from DDSM database. Comparing with the original optimal filter-based technique, our technique reduces the false detections (FD), eliminates the influence of the background in mammograms and is able to adaptively select the threshold for the detection of microcalcifications.
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
Zheng, B., Hardesty, L.A., Poller, W.R., Sumkin, J.H., Golla, S.: Mammography with computer-aided detection: reproducibility assessment - initial experience. Radiology 228(1), 58–62 (2003)
Destounls, S.V., Dinitto, P., Logan-Young, W., Bonacclo, E., Zuley, M.L., Willison, K.M.: Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? initial experience. Radiology 232, 578–584 (2004)
Soo, M.S., Rosen, E.L., Xia, J.Q., Ghate, S., Baker, J.A.: Computer-aided detection of amorphous calcifications. American Journal of Roentgenology 184(3), 887–892 (2005)
Randen, T., Husoy, J.H.: Texture segmentation using filters with optimized energy separation. IEEE Transactions on Image Processing 8(4), 571–582 (1999)
Gulsrud, T.O., Husoy, J.H.: Optimal filter-based detection of microcalcifications. IEEE Transactions on Biomedical Engineering 48(11), 1272–1281 (2001)
Gulsrud, T.O., Mestad, E.: Perprocessing techniques for improved segmentation of clustered microcalcifications in digital mammograms. In: 2nd International GABOR Workshop in Vienna (2001)
Netsch, T., Peitgen, H.O.: Scale-space signatures for the detection of clustered microcalcifications in digital Mammograms. IEEE Transactions on Medical Imaging 18(9), 774–786 (1999)
Cheng, H.D., Wang, J.L., Shi, X.J.: Microcalcification detection using fuzzy logic and scale space approaches. Pattern Recognition 37(2), 363–375 (2004)
Bocchi, L., Coppini, G., Nori, J., Valli, G.: Detection of single and clustered micro calcifications in mammograms using fractals models and neural networks. Medical Engineering & Physics 26(4), 303–312 (2004)
El-Naqa, I., Yang, Y., Wernick, M.N., Galatsanos, N.P., Nishikawa, R.M.: A Support Vector Machine Approach for Detection of Microcalcifications. IEEE Transactions on Medical Imaging 21(12), 1552–1563 (2002)
Suckling, J., et al.: The Mammographic Image Analysis Society digital mammogram database. In: Proceedings of the 2nd International Workshop on Digital Mammography, pp. 375–378 (1994)
Qian, W., Mao, F., Sun, X., Zhang, Y., Song, D., Clarke, R.A.: An improved method of region grouping for microcalcification detection in digital mammograms. Computerized Medical Imaging and Graphics 26, 361–368 (2002)
Kallergi, M.: Computer-aided diagnosis of mammographic microcalcification clusters. Medical Physics 31(2), 314–326 (2004)
Heine, J.J., Deans, S.R., Cullers, D.K., Stauduhar, R., Clarke, L.P.: Multiresolution statistical analysis of high-resolution digital mammograms. IEEE Transactions on Medical Imaging 16(5), 503–515 (1997)
Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., Munishkumaran, S.: Current status of the digital database for screening mammography. Digital Mammography, 457–460 (1998)
Chakraborty, D.P., Winter, L.H.L.: Free-response methodology: Alternate analysis and a new observer-performance experiment. Radiology 174, 873–881 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Z.Q., Jiang, J., Peng, Y.H., Gulsrud, T.O. (2006). A Filter-Based Approach Towards Automatic Detection of Microcalcification. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_57
Download citation
DOI: https://doi.org/10.1007/11783237_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
eBook Packages: Computer ScienceComputer Science (R0)