Abstract
A method is proposed based on the combination of wavelet analysis and principal component analysis (PCA). Microcalcification (MC) candidate regions are initially labeled using area and contrast criteria. Mallat’s redundant dyadic wavelet transform is used to analyze the frequency content of image patterns at horizontal and vertical directions. PCA is used to efficiently encode MC patterns in wavelet-decomposed images. Feature weights are computed from the projection of each candidate MC pattern at the wavelet-based principal components. To assess the effectiveness of the proposed method, the same analysis is carried out in original images. Candidate MC patterns are classified by means of Linear Discriminant Analysis (LDA). Free-response Receiver Operating Characteristic (FROC) curves are produced for identifying MC clusters. The highest performance is obtained when PCA is applied in wavelet decomposed images achieving 80% sensitivity at 0.5 false positives per image in a dataset with 50 subtle MC clusters in dense parenchyma.
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Kopans, D.B.: The Positive Predictive Value of Mammography. Am. J. Roentgenol. 158, 521–526 (1992)
Jackson, V.P., Hendrick, R.E., Feig, S.A., Kopans, D.B.: Imaging of the Radiographically Dense Breast. Radiology 188, 297–301 (1993)
Chan, H.-P., Sahiner, B., Petrick, N., Hadjiiski, L., Paquerault, S.: Computer-Aided Diagnosis of Breast Cancer. In: Costaridou, L. (ed.) Medical Image Analysis Methods, pp. 1–49. CRC Press, Taylor & Francis Group, Boca Raton (2005)
Wei, L., Yang, Y., Nishikawa, R.M., Wernick, M.N., Edwards, A.: Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications. IEEE Trans. Med. Imag. 24, 1278–1285 (2005)
Sampat, P.M., Markey, M.K., Bovik, A.C.: Computer-Aided Detection and Diagnosis in Mammography. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing, 2nd edn., pp. 1195–1217. Academic Press, London (2005)
Nishikawa, R.M.: Detection of Microcalcifications. In: Strickland, R.N. (ed.) Image-Processing Techniques for Tumor Detection, pp. 131–153. Marcel Dekker, New York (2002)
Samei, E., Eyler, W., Baron, L.: Effects of Anatomical Structure on Signal Detection. In: Beutel, J., Kundel, H.L., Van Meter, R.L. (eds.) Handbook of Medical Imaging. Physics and Psychophysics, vol. 1, pp. 655–682. SPIE Press, Bellingham, Washington (2000)
Netsch, T., Peitgen, H.O.: Scale-Space Signatures for the Detection of Clustered Microcalcifications in Digital Mammograms. IEEE Trans. Med. Imag. 18, 774–786 (1999)
Strickland, R.N., Hee, H.: Wavelet Transforms for Detecting Microcalcifications in Mammograms. IEEE Trans. Med. Imag. 15, 218–229 (1996)
Yoshida, H., Doi, K., Nishikawa, R.M., Giger, M.L., Schmidt, R.A.: An Improved Computer-Assisted Diagnostic Scheme Using Wavelet Transform for Detecting Clustered Microcalcifications in Digital Mammograms. Acad. Radiol. 3, 621–627 (1996)
Drexl, J., Heinlein, P., Schneider, W.: MammoInsight Computer Assisted Detection: Performance Study with Large Database. In: Bildverarbeitung fur die Medizin, Springer, Heidelberg (2003)
Qian, W., Kallergi, M., Clarke, L.P., Li, H.D., Venugopal, P., Song, D., Clark, R.A.: Tree Structured Wavelet Transform Segmentation of Microcalcifications in Digital Mammography. Med. Phys. 22, 1247–1254 (1995)
Lado, M.J., Tahoces, P.G., Mendez, A.J., Souto, M., Vidal, J.J.: A Wavelet-Based Algorithm for Detecting Clustered Microcalcifications in Digital Mammograms. Med. Phys. 26, 1294–1305 (1999)
Costaridou, L., Sakellaropoulos, P., Stefanoyiannis, A.P., Ungureanu, E., Panayiotakis, G.: Quantifying Image Quality at Breast Periphery vs Mammary Gland in Mammography Using Wavelet Analysis. Br. J. Radiol. 74, 913–919 (2001)
Chan, H.P., Sahiner, B., Lam, K.L., Petrick, N., Helvie, M.A., Goositt, M.M., Adler, D.D.: Computerized Analysis of Mammographic Microcalcifications in Morphological and Texture Feature Spaces. Med. Phys. 25, 2007–2019 (1998)
Mallat, S.G.: Wavelet Tour of Signal Processing, 2nd edn. Academic Press, San Diego (1999)
Laine, A.F., Schuler, S., Jian, F., Huda, W.: Mammographic Feature Enhancement by Multiscale Analysis. IEEE Trans. Med. Imag. 13, 725–740 (1994)
Zhang, W., Yoshida, H., Nishikawa, R.M., Doi, K.: Optimally Weighted Wavelet Transform Based on Supervised Training for Detection of Microcalcifications in Digital Mammograms. Med. Phys. 25, 949–956 (1998)
Van Belle, G., Fisher, L.D., Heagerty, P.J., Lumley, T.: Biostatistics: A Methodology for the Health Sciences, 2nd edn., pp. 584–639. John Wiley & Sons Inc., Hoboken, New Jersey (2004)
Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
Veldkamp, W.J.H., Karssemeijer, N.: Normalization of Local Contrast in Mammograms. IEEE Trans. Med. Imag. 19, 731–738 (2000)
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Arikidis, N., Skiadopoulos, S., Sakellaropoulos, F., Panayiotakis, G., Costaridou, L. (2006). Capturing Microcalcification Patterns in Dense Parenchyma with Wavelet-Based Eigenimages. 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_73
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DOI: https://doi.org/10.1007/11783237_73
Publisher Name: Springer, Berlin, Heidelberg
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