Digital breast tomosynthesis imaging using total variation and non-local means

2015 ◽  
Author(s):  
Metin Ertas ◽  
Aydin Akan ◽  
Isa Yildirim ◽  
Ali Dinler ◽  
Mustafa Kamasak
Keyword(s):  
Total Variation ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Local Means ◽  
Non Local

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Phaneendra K. Yalavarthy ◽  
Sandeep Kumar Kalva ◽  
Manojit Pramanik ◽  
Jaya Prakash
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Local Means ◽  
Non Local

Total Variation Regularization in Digital Breast Tomosynthesis

2013 ◽  
pp. 356-361
Author(s):  
Sascha Fränkel ◽  
Katrin Wunder ◽  
Ulrich Heil ◽  
Daniel Groß ◽  
Ralf Schulze ◽  
...  
Keyword(s):  
Total Variation ◽  
Digital Breast Tomosynthesis ◽  
Total Variation Regularization ◽  
Breast Tomosynthesis

scholarly journals An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering

2014 ◽  
Vol 13 (1) ◽  
pp. 65 ◽  
Author(s):  
Metin Ertas ◽  
Isa Yildirim ◽  
Mustafa Kamasak ◽  
Aydin Akan
Keyword(s):  
Total Variation ◽  
Local Means ◽  
Non Local

scholarly journals Use of a Total Variation Minimization Iterative Reconstruction Algorithm to Evaluate Reduced Projections during Digital Breast Tomosynthesis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Tsutomu Gomi ◽  
Yukio Koibuchi
Keyword(s):  
Total Variation ◽  
Convex Sets ◽  
Iterative Algorithms ◽  
Digital Breast Tomosynthesis ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Lesion Size ◽  
Reconstruction Technique ◽  
Breast Tomosynthesis ◽  
Total Variation Minimization

Purpose. We evaluated the efficacies of the adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP), and maximum likelihood expectation maximization (MLEM) total variation minimization iterative algorithms for reducing exposure doses during digital breast tomosynthesis for reduced projections. Methods. Reconstructions were evaluated using normal (15 projections) and half (i.e., thinned-out normal) projections (seven projections). The algorithms were assessed by determining the full width at half-maximum (FWHM), and the BR3D Phantom was used to evaluate the contrast-to-noise ratio (CNR) for the in-focus plane. A mean similarity measure of structural similarity (MSSIM) was also used to identify the preservation of contrast in clinical cases. Results. Spatial resolution tended to deteriorate in ASD-POCS algorithm reconstructions involving a reduced number of projections. However, the microcalcification size did not affect the rate of FWHM change. The ASD-POCS algorithm yielded a high CNR independently of the simulated mass lesion size and projection number. The ASD-POCS algorithm yielded a high MSSIM in reconstructions from reduced numbers of projections. Conclusions. The ASD-POCS algorithm can preserve contrast despite a reduced number of projections and could therefore be used to reduce radiation doses.

3D Digital Breast Tomosynthesis Using Total Variation Regularization

2008 ◽  
pp. 621-627 ◽  
Author(s):  
Iason Kastanis ◽  
Simon Arridge ◽  
Alex Stewart ◽  
Spencer Gunn ◽  
Christer Ullberg ◽  
...  
Keyword(s):  
Total Variation ◽  
Digital Breast Tomosynthesis ◽  
Total Variation Regularization ◽  
Breast Tomosynthesis

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

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