Research Article: PCANet based nonlocal means method for speckle noise removal in ultrasound images

Date Published: October 12, 2018

Publisher: Public Library of Science

Author(s): Houqiang Yu, Mingyue Ding, Xuming Zhang, Jinbo Wu, Zhangyang Wang.

http://doi.org/10.1371/journal.pone.0205390

Abstract

Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.

Partial Text

Medical imaging plays a critical role in disease monitoring and diagnosis. Compared with other imaging techniques such as X-ray, CT and MRI, ultrasound imaging is a noninvasive, real-time and radiation-free imaging modality. However, the ultrasound images are inevitably corrupted by speckle noise due to the coherent imaging mechanism from the scatters [1]. Such a noise reduces the sharpness of image details and complicates the diagnosis of the tiny structure of lesions. Therefore, despeckling is of great significance for improving ultrasound image quality. Moreover, as a pre-processing step, denoising will also benefit image post-processing tasks such as image segmentation, classification and registration.

In this section, the proposed method is tested on the synthetic image, the simulated image and the real ultrasound images. To demonstrate the superiority of the proposed PCANet based NLM method (PPCA-NLM), it will be compared with such traditional well-known despeckling algorithms as Frost, Kuan, SBF, SRAD, TNLM, OBNLM and NLMLS. Meanwhile, the proposed method will be also compared with the DnCNN method and the NLM methods using the original PCANet model (OPCA-NLM), the ReLU-based PCANet model (RPCA-NLM) and the sigmoid-based PCANet model (SPCA-NLM). In all experiments, the window size of Frost and Kuan filters is fixed to be 3×3. For SBF and SRAD filters, the parameters are fine-tuned as referred in [10, 11]. The sizes of similarity window and search window are set as 7×7 and 17×17 in the TNLM, OBNLM, NLMLS and PCANet based NLM filters, respectively. For the DnCNN denoiser, the same database is used as the PCANet to train the DnCNN model based on Eq (1), and the parameters and network structures are set as suggested in [31]. For these NLM-based filters, the decay parameter is determined using the rule-of-thumb, i.e., h = β·σ, where β and σ denote a predefined constant and the noise standard deviation [38], respectively. In these PCANet based NLM methods, the number of PCA filters in the two convolutional layers is fixed to be 12 and the patch size is 7×7, respectively.

In this paper, the modified PCANet based deep learning baseline is introduced into NLM method for reducing speckle noise in the ultrasound images. This proposed method utilizes the intrinsic features extracted from an input noisy image by the PCANet to refine the similarity computation in the NLM method. The introduction of the intrinsic features instead of the gray-level information into the NLM method can facilitate the effective despeckling of ultrasound images due to its effectiveness in representing their structural information. The experimental results demonstrate that the proposed method outperforms the other compared methods in speckle reduction and image detail preservation. Therefore, the proposed approach has the great potential application to ultrasound-based clinical diagnosis. In future, the new deep learning models will be developed to denoise the images corrupted by Rician noise or Poisson noise.

 

Source:

http://doi.org/10.1371/journal.pone.0205390

 

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