Abstract
An adaptive similarity measure (ASM) is designed to improve automated identification of corresponding lesions on prior mammograms. It combines adaptive filtering to enhance the lesion and a similarity measure (SM) as a figure-of-merit (FOM) measure. The filters are designed with a training set to maximize and minimize the FOM for the similar and dissimilar image pairs, respectively, by using a gradient optimization technique. The ASM was applied to the final stage of our multistage regional registration technique for identification of the mass on the prior mammogram. A search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within a search region, guided by the ASM. This new approach was evaluated by using 179 temporal pairs of mammograms containing biopsy-proven masses. 86% of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations. The average distance between the estimated and the true lesion centroids on the prior mammogram was 4.5 ± 6.7 mm. In comparison, the correct localization and the average distance using a conventional correlation SM were 84% and 4.9 ±7.0 mm, respectively. The ASM improved the identification of the corresponding lesions on temporal pairs of mammograms.
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References
Zuckerman, H.C., The role of mammography in the diagnosis of breast cancer, in Breast Cancer, Diagnosis and Treatment, I.M. Ariel and J.B. Cleary, Editors. 1987, McGraw-Hill: New York. p. 152–172.
Tabar, L. and P.B. Dean, The Control of Breast Cancer through Mammography Screening. Radiologic Clines of North America, 1987. 25: p. 961.
Bassett, L.W., B. Shayestehfar, and I. Hirbawi, Obtaining previous mammograms for comparison: usefullness and costs. Amer. J. Roentgenology, 1994. 163(5): p.1083–1086.
Sickles, E.A., Periodic mammographie follow-up of probably benign lesions: results in 3183 consecutive cases. Radiology, 1991. 179: p. 463–468.
Sallam, M. and K. Bowyer, Detecting abnormal densities in mammograms by comparison with previous screenings, in Digital Mammography ’96, K. Doi, et al., Editors. 1996, Elsevier: Amsterdam, p. 417–420.
Brzakovic, D., et al., Mammogram Analysis by Comparison with Previous Screenings, in Digital Mammography, A.G. Gale, et al., Editors. 1994, Elsevier: Amsterdam, p. 131–140.
Vujovic, N. and D. Brzakovic, Establishing the correspondence between control points in pairs of mammographic images. IEEE Transactions on Medical Imaging, 1997. 6: p. 1388–1399.
Gopal, S.S., et al., A regional registration technique for automated analysis of interval changes of breast lesions. Proc. SPIE, 1998. 3338: p. 118–131.
Gopal, S.S., et al., A regional registration technique for automated interval change analysis of breast lesions on mammograms. Medical Physics, 1999. 26: p. 2669–2679.
Hadjiiski, L.M., et al., Automated identification of breast lesions in temporal pairs of mammograms for interval change analysis. Radiology, 1999. 213 (P): p. 229–230.
Hadjiiski, L.M., et al., Interval change analysis in temporal pairs of mammograms using a local affine transformation. Proc. SPIE, 2000. 3979: p. 847–853.
Hadjiiski, L.M., et al., Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis — local affine transformation for improved localization Medical Physics, 2001. 28 (6): p. 1070–1079.
Good, W.F., et al., Generalized procrustean image deformation for substraction of mammograms. Proc. SPIE, 1999. 3661: p. 1562–1573.
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Hadjiiski, L., Sahiner, B., Heang-Ping, C., Petrick, N., Helvie, M.A. (2003). An Adaptive Similarity Measure for Automated Identification of Breast Lesions in Temporal Pairs of Mammograms for Interval Change Analysis. In: Peitgen, HO. (eds) Digital Mammography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59327-7_71
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DOI: https://doi.org/10.1007/978-3-642-59327-7_71
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