Noise Removal Using Nonlinear Anisotropic Diffusion Filtering Based on Statistic-Local Open System

2008 ◽  
Author(s):  
Weixin Wu ◽  
Hongchen Liu
Keyword(s):  
Open System ◽  
Anisotropic Diffusion ◽  
Noise Removal ◽  
Diffusion Filtering ◽  
Nonlinear Anisotropic Diffusion

scholarly journals Lung Segmentation and Iterative Weighted Averaging Smoothing Technique on Chest Ct Images

2019 ◽  
Vol 9 (1S3) ◽  
pp. 383-387
Keyword(s):  
Computed Tomography ◽  
Anisotropic Diffusion ◽  
Clustering Algorithm ◽  
Chest Ct ◽  
Weighted Averaging ◽  
Lung Tumour ◽  
Lung Segmentation ◽  
Averaging Technique ◽  
Diffusion Filtering ◽  
Nonlinear Anisotropic Diffusion

Computed Tomography (CT) is one of the most commonly used imaging modalities for tumour detection and diagnosis, due to its high spatial resolution, fast imaging speed and wide availability. Nodules of the lungs and pathological residues with varied diameter can be comfortably viewed by computed tomography and can be categorized as benign or malignant. The key intention of this segmentation and smoothing is to develop an efficient methodology for an automated lung tumour diagnosis. Segmentation is the quantitative preprocessing step used in the chest CT analysis. The iterative weighted averaging technique is used in addressing the issues related to the segmentation and smoothing method employed in this paper. The methodology incorporates the accurate Lung Segmentation, enclosure of Juxtapleural nodules, the proper insertion of the bronchial vessels for enhancing the smoothness of the segmented Lung area. The steps involved in this paper include Image preprocessing by nonlinear anisotropic diffusion filtering, Thorax Extraction, Lung segmentation and iterative weighted averaging technique for smoothing the contours. The main feature in choosing the nonlinear anisotropic diffusion filtering is for properly preserving the regions unaffected with cancer and also to differentiate the artefacts involved due to the subject movements. In the fuzzy c- means clustering algorithm, the lung parenchyma is identified from the thorax region based on the fuzzy membership value and the affected regions are attached. The standard execution time for segmentation process of one dataset is 1.91s, it is the more rapid process than the manual segmentation.

Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering

2017 ◽  
Vol 27 (3) ◽  
pp. 248-264 ◽  
Author(s):  
Chandrajit Pal ◽  
Pabitra Das ◽  
Amlan Chakrabarti ◽  
Ranjan Ghosh
Keyword(s):  
Anisotropic Diffusion ◽  
Noise Removal ◽  
Rician Noise ◽  
Diffusion Filtering

scholarly journals Nonlinear anisotropic diffusion filtering of three-dimensional image data from two-photon microscopy

2004 ◽  
Vol 9 (6) ◽  
pp. 1253 ◽  
Author(s):  
Philip. J. Broser ◽  
R. Schulte ◽  
S. Lang ◽  
A. Roth Fritjof ◽  
Helmchen ◽  
...  
Keyword(s):  
Anisotropic Diffusion ◽  
Three Dimensional ◽  
Image Data ◽  
Dimensional Image ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Diffusion Filtering ◽  
Nonlinear Anisotropic Diffusion

scholarly journals An Efficient FPGA Implementation of Optimized Anisotropic Diffusion Filtering of Images

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chandrajit Pal ◽  
Avik Kotal ◽  
Asit Samanta ◽  
Amlan Chakrabarti ◽  
Ranjan Ghosh
Keyword(s):  
Anisotropic Diffusion ◽  
Noise Removal ◽  
Fpga Implementation ◽  
Medical Surveillance ◽  
Simple Arithmetic ◽  
Space Applications ◽  
Edge Preserving ◽  
Anisotropic Diffusion Filter ◽  
Diffusion Filtering ◽  
Reconstruction Quality

Digital image processing is an exciting area of research with a variety of applications including medical, surveillance security systems, defence, and space applications. Noise removal as a preprocessing step helps to improve the performance of the signal processing algorithms, thereby enhancing image quality. Anisotropic diffusion filtering proposed by Perona and Malik can be used as an edge-preserving smoother, removing high-frequency components of images without blurring their edges. In this paper, we present the FPGA implementation of an edge-preserving anisotropic diffusion filter for digital images. The designed architecture completely replaced the convolution operation and implemented the same using simple arithmetic subtraction of the neighboring intensities within a kernel, preceded by multiple operations in parallel within the kernel. To improve the image reconstruction quality, the diffusion coefficient parameter, responsible for controlling the filtering process, has been properly analyzed. Its signal behavior has been studied by subsequently scaling and differentiating the signal. The hardware implementation of the proposed design shows better performance in terms of reconstruction quality and accelerated performance with respect to its software implementation. It also reduces computation, power consumption, and resource utilization with respect to other related works.

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