Orea-Flores Izlian Y, Gallegos-Funes Francisco J, Arellano-Reynoso Alfonso
Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional Av. IPN s/n, Edificio Z, acceso 3, 3er piso; SEPI-Electrónica, Col. Lindavista, 07738 Ciudad de México, Mexico.
Instituto Nacional de Neurología y Neurocirugía, Av. Insurgentes Sur 3877, Col. La Farma, 14269 Ciudad de México, Mexico.
Entropy (Basel). 2019 Apr 16;21(4):401. doi: 10.3390/e21040401.
In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.
在本文中，我们提出了一种基于局部复杂度估计的小波域滤波方法用于磁共振成像（MRI）去噪。提出了一种阈值选择方法，其中每个像素的边缘和细节保留特性由输入图像的局部复杂度决定。在所提出的滤波方法中，将当前小波核与一个阈值进行比较，以在一个尺度上识别信号主导或噪声主导的像素，从而提供良好的视觉质量，避免处理后的图像出现模糊和过度平滑的情况。我们对不同小波进行了比较性能分析，以找到用于MRI去噪的最优小波。在受莱斯噪声退化的模拟MR图像上进行的数值实验和视觉结果表明，所提出的算法通过在噪声抑制和精细细节保留之间取得平衡，始终优于其他去噪方法。所提出的算法可以增强区域之间的对比度，从而在处理后的图像中勾勒出不同纹理或组织之间的感兴趣区域。所提出的方法在实际MRI去噪中通过平衡细节保留和噪声去除、增强图像区域之间的对比度，产生了令人满意的结果。此外，在所提出的算法与其他方法在加性高斯白噪声（AWGN）情况下使用标准图像进行比较，以证明所提出的方法不需要专门针对莱斯噪声或AWGN噪声进行调整；这是所提出的方法相对于其他方法的一个优势。最后，所提出的方案对于MRI去噪来说简单、高效且可行。
