scholarly journals Noise removal for medical X-ray images in wavelet domain

2008 ◽  
Vol 163 (3) ◽  
pp. 37-46 ◽  
Author(s):  
Ling Wang ◽  
Jianming Lu ◽  
Yeqiu Li ◽  
Takashi Yahagi ◽  
Takahide Okamoto
Keyword(s):  
Noise Removal ◽  
Wavelet Domain ◽  
X Ray

scholarly journals Noise Removal for Medical X-ray Images in Wavelet Domain

2006 ◽  
Vol 126 (2) ◽  
pp. 237-244 ◽  
Author(s):  
Ling Wang ◽  
Jianming Lu ◽  
Yeqiu Li ◽  
Takashi Yahagi ◽  
Takahide Okamoto
Keyword(s):  
Noise Removal ◽  
Wavelet Domain ◽  
X Ray

scholarly journals Fuzzy genetic-based noise removal filter for digital panoramic X-ray images

2018 ◽  
Vol 38 (4) ◽  
pp. 941-965 ◽  
Author(s):  
Mehravar Rafati ◽  
Fateme Farnia ◽  
Mahdi Erfanian Taghvaei ◽  
Ali Mohammad Nickfarjam
Keyword(s):  
Noise Removal ◽  
X Ray ◽  
Fuzzy Genetic

scholarly journals Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 401 ◽  
Author(s):  
Izlian Y. Orea-Flores ◽  
Francisco J. Gallegos-Funes ◽  
Alfonso Arellano-Reynoso
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Additive White Gaussian Noise ◽  
Noise Removal ◽  
Wavelet Domain ◽  
Resonance Imaging ◽  
Comparative Performance ◽  
Filtering Method ◽  
Local Complexity ◽  
Complexity Estimation

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.

Background noise removal in x-ray ptychography

2017 ◽  
Vol 56 (8) ◽  
pp. 2099 ◽  
Author(s):  
Chunpeng Wang ◽  
Zijian Xu ◽  
Haigang Liu ◽  
Yong Wang ◽  
Jian Wang ◽  
...  
Keyword(s):  
Background Noise ◽  
Noise Removal ◽  
X Ray

Noise reduction for low-dose X-ray CT based on fuzzy logical in stationary wavelet domain

Optik ◽  
2013 ◽  
Vol 124 (18) ◽  
pp. 3348-3352 ◽  
Author(s):  
Yi Liu ◽  
Zhiguo Gui ◽  
Quan Zhang
Keyword(s):  
Noise Reduction ◽  
Low Dose ◽  
Wavelet Domain ◽  
X Ray ◽  
Fuzzy Logical

Noise removal of the x-ray medical image using fast spatial filters and GPU

2018 ◽  
Author(s):  
Luis Cadena ◽  
Alexander Zotin ◽  
Franklin Cadena ◽  
Nikolai Espinosa
Keyword(s):  
Medical Image ◽  
Noise Removal ◽  
Spatial Filters ◽  
X Ray

scholarly journals X-ray CT Metal Artifact Reduction Using Wavelet Domain $L_{0}$ Sparse Regularization

2013 ◽  
Vol 32 (9) ◽  
pp. 1707-1722 ◽  
Author(s):  
Abolfazl Mehranian ◽  
Mohammad Reza Ay ◽  
Arman Rahmim ◽  
Habib Zaidi
Keyword(s):  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Wavelet Domain ◽  
Sparse Regularization ◽  
X Ray
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