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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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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