Abstract
Wavelet-based channel energy with low cost and high efficacy is a valuable feature for the differential diagnoses between benign and malignant breast lesions. The new feature is a contour approach that generally suffers from lacking a reliable contour detection algorithm with convincing results due to extreme noise. For investigating a procedure suitable for clinical application, noise resistance capability of the new feature is evaluated in this study. The evaluation system consists of two snake-based contour detection algorithms associated with two pre-processes. These combinations can produce four test datasets of contour sonogram. Classification performance evaluation is based on a probabilistic neural network and a genetic algorithm used for distribution parameter determination.
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References
Kolb, T.M., Lichy, J., Newhouse, J.H.: Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27825 patient evaluations. Radiology 225, 165–175 (2002)
Gefen, S., Tretiak, O.J., Piccoli, C.W., Donohue, K.D., Petropulu, A.P., Shankar, P.M., Dumane, V.A., Huang, L., Kutay, M.A., Genis, V., Forsberg, F., Reid, J.M., Goldberg, B.B.: ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis. IEEE Trans. Med. Imag. 22, 170–177 (2003)
Chen, D.R., Chang, R.F., Kuo, W.J., Chen, M.C., Huang, Y.L.: Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med. Biol. 28, 1301–1310 (2002)
Lee, H.W., Liu, B.D., Hung, K.C., Lei, S.F., Wang, P.C., Yang, T.L.: Breast tumor classification of ultrasound images using wavelet-based channel energy and ImageJ. IEEE Journal of Selected Topics in Signal Processing 3, 81–93 (2009)
Liao, Y.-C., Hung, K.-C., Ku, C.-T., Tsai, C.-F., Guo, S.-M.: Wavelet Octave Energy for Breast Tumor Classification on Sonography: A New Shape Feature. In: Proc. IEEE International Conference on Networking, Sensing and Control (ICNSC 2009), pp. 388–392 (March 2009)
Alvarenga, A.V., Pereira, W.C.A., Infantosi, A.F.C., de Azevedo, C.M.: Classification of breast tumours on ultrasound images using morphometric parameters. In: Proc. IEEE Int. Symp. on Intell. Signal Processing, pp. 206–210 (September 2005)
Andrey, P., Boudier, T.: Adaptive Active Contours. In: ImageJ Conference (2006)
Jacob, M., Blu, T., Unser, M.: A Unifying Approach and Interface for Spline-Based Snakes. In: Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing, San Diego CA, USA, February 17-22. Part I, vol. 4322, pp. 340–347 (2001)
Jacob, M., Blu, T., Unser, M.: Efficient energies and algorithms for parametric snakes. IEEE Transactions on Image Processing 13(9), 1231–1244 (2004)
Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1) (1990) ISSN:0893-6080
Farrokhrooz, M., Karimi, M., Rafiei, A.: A new method for spread value estimation in multi-spread PNN and its application in ship noise classification. In: 9th International Symposium on Signal Processing and Its Applications, ISSPA, pp. 1–4 (2007)
Eramian, M.G., Adams, G.P., Pierson, R.A.: Enhancing ultrasound texture differences for developing an in vivo ’virtual histology’ approach to bovine ovarian imaging. Reproduction, Fertility and Development 19(8), 910–924 (2007)
Holland, J.H.: Genetic Algorithms. In: SCI. A.M., pp. 66–72 (July 1992)
Yu, S., Guan, L.: A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging 19(2), 115–126 (2000)
Shah, V.P., Bruce, L.M., Younan, N.H.: Applying modular classifiers to mammographic mass classification. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS 2004, vol. 1, pp. 1585–1588 (2004)
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Liao, YC., Guo, SM., Hung, KC., Wang, PC., Yang, TL. (2010). Noise Resistance Analysis of Wavelet-Based Channel Energy Feature for Breast Lesion Classification on Ultrasound Images. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_51
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DOI: https://doi.org/10.1007/978-3-642-15696-0_51
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