Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary
AbstractUltrasound images are corrupted with multiplicative noise known as speckle, which reduces the effectiveness of image processing and hampers interpretation. This paper proposes a multiplicative speckle suppression technique for ultrasound liver images, based on a new signal reconstruction model known as sparse representation (SR) over dictionary learning. In the proposed technique, the non-uniform multiplicative signal is first converted into additive noise using an enhanced homomorphic filter. This is followed by pixel-based total variation (TV) regularization and patch-based SR over a dictionary trained using K-singular value decomposition (KSVD). Finally, the split Bregman algorithm is used to solve the optimization problem and estimate the de-speckled image. The simulations performed on both synthetic and clinical ultrasound images for speckle reduction, the proposed technique achieved peak signal-to-noise ratios of 35.537 dB for the dictionary trained on noisy image patches and 35.033 dB for the dictionary trained using a set of reference ultrasound image patches. Further, the evaluation results show that the proposed method performs better than other state-of-the-art denoising algorithms in terms of both peak signal-to-noise ratio and subjective visual quality assessment. View Full-Text
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Jabarulla, M.Y.; Lee, H.-N. Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary. Appl. Sci. 2018, 8, 903.
Jabarulla MY, Lee H-N. Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary. Applied Sciences. 2018; 8(6):903.Chicago/Turabian Style
Jabarulla, Mohamed Y.; Lee, Heung-No. 2018. "Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary." Appl. Sci. 8, no. 6: 903.
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