Denoising 3D ultrasound volumes using sparse representation.

In this paper, a new 3D ultrasound (US) denoising technique that adopts the sparse representation has been proposed for an effective noise reduction in 3D US volumes. The purpose of the proposed method is to reduce image noise while preserving 3D objects edges, hence improving the human interpretation for clinical diagnosis and the 3D segmentation accuracy for further automatic malignancy detection. For denoising 3D US volumes, sparse representation was employed, which has showed an excellent performance in reducing Gaussian noise. It has been well known that US images contain severe multiplicative speckle noise, which has different characteristics compared to the additive Gaussian noise. In this paper, we propose a denoising framework for effectively reducing both Gaussian noise and speckle noise on 3D US volumes. The proposed method removes Gaussian noise using sparse representation. Then, a logarithmic transform is performed to transform the speckle noise into Gaussian noise for applying the sparse representation. To demonstrate the effectiveness of the proposed denoising method, comparative and quantitative experiments had been conducted on a synthesized 3D US phantom data. Experimental results showed that the proposed denoising could improve image quality in terms of denoising measurements.