Abstract
In this work we employ computer-vision techniques to detect natural biological symmetries in breast MR scans. Currently, breast MR images are assessed in terms of the kinetics and location of uptake of Gd-DTPA. However, mammographic interpretation often uses symmetry between left and right breasts to indicate the site of potential tumour masses but has not been used in breast MRI. In this study, we present such a method for characterizing breast symmetry based on three objective measures of similarity including multiresolution non-orthogonal wavelet representation, three-dimensional intensity distributions and co-occurrence matrices. Statistical feature distributions that are invariant to feature localization are computed for each of the similarity metrics. These distributions are later compared against each other to account for perceptual similarity. Studies based on 51 normal MRI scans of randomly selected patients showed that the sensitivity of symmetry detection rate approached 94%. The symmetry analysis procedure presented in this paper can be applied as an aid in detecting breast tissue changes arising from disease.
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Yin, F.F., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A.: Computerized detection of masses in digital mammograms: Automated alignment of breast images and its effect on bilateral-subtraction techniques. Medical Physics 21(3), 445–452 (1994)
Sallam, M.Y., Bowyer, K.W.: Registration and difference analysis of corresponding mammogram images. Medical Image Analysis 3(2), 103–118 (1999)
Vujovic, N., Brazkovic, D.: Establishing the correspondence between control points in pairs of mammographic images. IEEE Transactions Image Processing 6, 1388–1399 (1997)
Dunn, D., Higgins, W.E., Wakeley, J.: Texture segmentation using 2-D Gabor elementary functions. IEEE Trans. Pattern Analysis and Machine Intelligence 16, 130–149 (1994)
Pitiot, A., Toga, A.W., Ayache, N., Thompson, P.M.: Texture-Based MRI Segmentation with a Two-Stage Hybrid Neural Classifier. In: IEEE 2002 World Congress on Computational Intelligence and Neural Nets, Honolulu, HI, May 12-17 (2002)
Jones, P., Palmer, L.A.: An evaluation of the two-dimensional Gabor model of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58(6), 1187–1211 (1987)
Smith, J.R.: Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression, PhD dissertation, Graduate School of Arts and Sciences, Columbia University (1997)
Weldon, T.P., Higgins, W.E., Dunn, D.F.: Gabor Filter Design for Multiple Texture Segmentation. Optical Engineering 35(10), 2852–2863 (1996)
Jain, A.K., Bhattacharjee, S.K.: Address block location on envelopes using Gabor filters. Pattern Recognition 25(12), 1459–1477 (1992)
Ohanian, P.P., Dubes, R.C.: Performance evaluation for four classes of textural features. Pattern Recognition 25(8), 819–833 (1992)
Huet, B., Hancock, E.R.: Structural Indexing of Infra-Red images using Statistical Histogram Comparison. In: Third International Workshop on Image and Signal Processing (IWISP 1996), Manchester, UK, November 1996, pp. 4–7 (1996)
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© 2003 Springer-Verlag Berlin Heidelberg
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Alterson, R., Plewes, D.B. (2003). Exploring Symmetries in Breast MRI Scan. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_92
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DOI: https://doi.org/10.1007/978-3-540-39903-2_92
Publisher Name: Springer, Berlin, Heidelberg
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