Abstract
Computer assisted diagnostic (CAD) methods have been proposed as a “second opinion” strategy for breast cancer screening using digital mammography. The reported methods have included the detection of either microcalcification clusters or masses [1]. Mass detection poses a more difficult problem compared to microcalcification cluster detection because masses are often: (a) of varying size, shape, and density, (b) exhibit poor image contrast, (c) are highly connected to the surrounding parenchymal tissue density, particularly for spiculated lesions, and (d) are surrounded by non-uniform tissue background with similar characteristics [2]–[4]. The segmentation of masses and the computation of related pixel intensity, morphological, and directional texture features poses difficult problems in terms of improved feature extraction methods, as required for classification methods that distinguish masses from normal tissues. Improved and robust feature extraction has not been emphasizes in the literature. Examples include features such as mass shape or mass margin analysis, or spiculations for spiculated lesions [5]. Mass detection is proposed here as a good clinical model for the motivation for proposing a new class of adaptive CAD methods for image preprocessing, to improve feature extraction. The methods proposed are useful for other CAD applications such as the detection of microcalcifications and lung nodules.
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References
M. L. Giger, “Computer-aided diagnosis,” RSNA Syllabus: A Categorical Course in Physics, Technical Aspects of Breast Imaging, eds. AG Haus, MJ Yaffe, 283–298, 1993.
L. W. Bassett, D. H. Bunnell, R. H. Jahashahi, R. H. Gold, R. D. Arndt, and J. Linsman, “Breast cancer detection: One versus two views,” Radiology 165, 95–97, 1987.
E. A. Sickle, W. N. Weber, and H. B. Galvin, “Baseline screening mammography: One vs two views per breast,” American J. Roentgenol, 147, 1149–1153, 1986.
D. Wolverton, R. M Nishikawa, W. Zouras, K. Doi, “CAD in Digital Mammography: Computerized detection and classification of masses,” Digital Mammography, Gale A.G. et al, eds, Elsevier Science B.V., 1994.
W. P. Kegelmeyer, J. M. Pruneda, P. D. Bourland, A. Hillis, M. W. Riggs, M. L. Nipper, “Computer-aided mammographic screening for spiculated lesions,” Radiology, vol. 191, 331–337, 1994.
R. Gupta, P. E. Undrill, “The use of texture analysis to delineate suspicious masses in mammography,” Phys. Med. Biol., vol.40, 835–855, 1995.
N. Petrick, H. P. Chan, B. Sahiner, D. Wei, M. A. Helvie, M. M. Goodsitt and D. D. Adler, “Automated detection of breast masses on digital mammograms using a convolution neural network for morphological and texture classification,” Proc. of World Congress on Neural Networks, vol.2, 872–875, 1995.
D. Brzakovic, X. M. Luo, P. Brzakovic, “An approach to automated detection of tumors in mammograms,” IEEE Trans. Medical Imaging, vol.9, 233–241, 1990.
W. P. Kegelmeyer, “Evaluation of stellate lesion detection in a standard mammogram data set,” Proc. of SPIE vol. 1905, 787–793, 1993.
Z. Huo, M.L. Giger, C.J. Vyborny, U. Bick, P. Lu, D. E. Wolverton, and R. A. Schmidt, “Analysis of spiculation in the computerized classification of mammographic masses,” Medical Physics, 22(10), 1569–1579, Oct., 1995.
M.R. Anderberg, Clustering Analysis for Application, Academic Press, New York, NY, 1973.
N. Karssemeijer, “Recognition of stellate lesions in digital mammograms,” Digital Mammography. Gale A.G. et al, eds, Elsevier Science B.V., 1994.
N. Karssemeijer and G. M. te Brake, “Detection of stellate distortion in mammograms,” IEEE Trans. on Medical Imaging, vol.15, no.5, Oct. 1996.
W. Qian, L.P. Clarke, et al., “Digital mammography: M-channel quadrature mirror filters for microcalcification extraction,” Computerized Imaging and Graphics, Vol. 18, No.5, pp 301–314, Sept./Oct., 1994.
W. Qian, L.P. Clarke, “Hybrid M-channel wavelet transform methods: adaptive, automatic and digital X-ray sensor independent,” Medical Physics, vol. 22(6), 983–984, 1995.
L. Li, W. Qian and L.P. Clarke, “Digital mammography: CAD method for mass detection using multiresolution and multiorientation wavelet transforms,” Academic Radiology, Oct, 1997.
L. Li, W. Qian, F. Mao, L.P. Clarke, “Wavelet transform for directional feature extraction in medical imaging,” Proc. of Int. Conf. on Image Processing, 1997.
W. Qian, L. Li and L. P. Clarke, “Digital mammography: Wavelet based CAD method for mass detection,” Proc. of SPIE Medical Imaging, 1997.
W. Qian, L. P. Clarke, M. Kallergi, H. D. Li, R. P. Velthuizen, R. A. Clark, and M. L. Silbiger, “Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography,” Proc of the IS&T/SPIE Annual Symposium on Electronic Imaging, Science & Technology, San Jose, California, 1993.
W. Qian, L. P. Clarke, M. Kallergi, B. Y. Zheng, P. Venugopal, R. A. Clark, M. L. Silbiger, “Application of Wavelet Transform for Image Enhancement in Medical Imaging,” Intelligent Engineering Systems Through Artificial Neural Networks, ASME, vol. 4, 651–660, 1994.
W. Qian, L. P. Clarke, M. Kallergi, R. A. Clark, “Tree-structured nonlinear filters in digital mammography,” IEEE Trans. Med. Imag., vol. 13(1), 25–36, 1994
L. P. Clarke, B. Zheng, W. Qian, “Artificial Neural Network for Pattern Recognition in Mammography,” Invited paper: World Congress on Neural Networks, CA, June 4–9, 1994.
L. Li, W. Qian, LP. Clarke, “X-ray medical image processing using directional wavelet transform,” Proceeding of IEEE Int. Conf. on ASSP, vol.4, 1996.
W. Qian, L. P. Clarke, M. Kallergi, B. Zheng, R. A. Clark, “Wavelet Transform for Computer Assisted Diagnosis (CAD) for Digital Mammography,” IEEE Engineering in Medicine and Biology Magazine, Invited Paper, vol.14(5), 561–569, 1995.
W. Qian, L. P. Clarke, “Wavelet-based neural network with fuzzy-logic adaptivity for nuclear image restoration,” Proceedings of the IEEE, Special Issue on Applications of Neural Networks, Invited paper, vol.84, no. 10, 1996.
T. N. Pappas, “An adaptive clustering algorithm for image segmentation,” IEEE Trans. on Signal Processing, vol.40(2), 902–914, 1992.
M.R. Anderberg, Clustering Analysis for Application, Academic Press, New York, 1973.
H.D. Li, M. Kallergi, L. P. Clarke, V. K. Jain and R. A. Clark, “Markov random field for tumor detection in digital mammography,” IEEE Trans. Medical Imaging, vol. 14(3), 565–576, 1995.
A.K. Jain, Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs, 1989.
K. Doi, “Overview of state of the art and future requirements,” Session 5B, Image Processing and Computer Assisted Diagnosis (CAD), Office of Womon’s Health (OWH) Workshop on Digital Mammography, Washinton DC, June 1–2, 1997.
Velthuizen R.P. and Clarke L.P. “Image Standardization for Digital Mammography“ 8th International Workshop on Digital Mammography (IWDM’98) Nijmegen, Netherlands, June 7–10 1998
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Qian, W., Li, L., Clarke, L., Mao, F., Clark, R.A., Thomas, C.J. (1998). A Computer Assisted Diagnostic System for Mass Detection. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds) Digital Mammography. Computational Imaging and Vision, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5318-8_14
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DOI: https://doi.org/10.1007/978-94-011-5318-8_14
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