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Abstract
Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
Highlights
In medical imaging, high-quality images play a critical role in clinical diagnosis by enabling the clinicians to determine the state of the illness based on the structural details and functional characteristics of the images
Step 1: The Laplacian eigenmaps network (LEPNet) model is trained using 300 PRI-nonlocal means (NLM) filtered Magnetic resonance (MR) images obtained from the open
Step 1: The LEPNet model is trained using 300 prefiltered rotationally invariant nonlocal means (PRI-NLM) filtered MR images obtained from the open magnetic resonance imaging (MRI) database to learn the convolution kernels of two convolutional layers
Summary
High-quality images play a critical role in clinical diagnosis by enabling the clinicians to determine the state of the illness based on the structural details and functional characteristics of the images. Many imaging techniques have been developed in recent decades. Magnetic resonance imaging (MRI) has attracted much attention due to its advantages of high resolution, nonradiation, noninvasiveness, and high contrast with human tissues [1]. The MR images are inevitably corrupted by noise. It has been shown that the noise in the MR images is governed by Rician distribution [2]. Such noise may affect the clinical diagnosis by degrading the Sensors 2019, 19, 2918; doi:10.3390/s19132918 www.mdpi.com/journal/sensors
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