Abstract
This paper presents a computer-aided system for speckle noise analysis in ultrasound images. The proposed system uses the combination of convolutional neural network (CNN) features and wavelet features to detect speckle noise in ultrasound images. The wavelet features are based on the covariance of the second-order statistical measures over the wavelet transform. Evaluations on standard databases show that the proposed system is gaining an accuracy of 98.30%, sensitivity 98.79%, and specificity of 98.52%. This approach is supported by a linear discriminate analysis (LDA) for characterization of object regions from noise regions. It produces a strong speckle reduction and edge preservation due to noise-free feature extraction scheme. The experimental result is compared with several other existing speckle reduction methods and it outperforms the state-of-the-art methods on the basis of contrast resolution and MSE.
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Acknowledgements
We are very grateful to Dr. Md. Farhan Matin, Associate Professor, Department of Radiology and Imaging, Uttara Adhunik Medical College & Hospital; for his valuable support, suggestions, and consultancy.
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Mostafiz, R., Islam, M.M., Rahman, M.M. (2020). Computer-Aided Speckle Noise Analysis in Ultrasound Images Through Fusion of Convolutional Neural Network and Wavelet Transform with Linear Discriminate Analysis. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_16
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DOI: https://doi.org/10.1007/978-981-13-7564-4_16
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