• Journal of the Korean Society of Radiology
• /
• v.13 no.5
• /
• pp.773-778
• /
• 2019

Images using X-rays are essential to diagnosis, but noise is inevitable in the image. To compensate for this, a total variation (TV) algorithm was presented to reduce the patient's exposure dose while increasing the quality of the images. The purpose of this study is to verify the effect on the image quality in radiographic imaging according to the thickness of the additional filtration plate through simulation, and to evaluate the usefulness of the TV algorithm. By using the Geant4 Application for Tomographic Emissions (GATE) simulation image, the actual size, shape and material of the Polymethylmethacrylate (PMMA) phantom were identical, the contrast to noise ratio (CNR) and coefficient of variation (COV) were compared. The results showed that the CNR value was the highest and the COV the lowest when applying the TV algorithm. In addition, we can acquire superior CNR and COV results with 0 mm Al in all algorithm cases.

• Journal of the Korean Society of Radiology
• /
• v.13 no.5
• /
• pp.765-771
• /
• 2019

Noise in skull X-ray imaging is inevitable, which reduces imaging quality and diagnostic accuracy and increases errors due to the nature of digital imaging devices. Increasing the dose can attenuate noise, but that could lead to big problems with higher exposure dose received by patients. Thus, noise reduction algorithms are actively being studied at low doses to solve dose problems and reduce noise at the same time. Wiener filter and median filter have been widely used, with the disadvantages of poor noise reduction efficiency and loss of much information about imaging boundary. The purpose of this study is to apply total variation (TV) algorithm to skull X-ray imaging that can compensate for the problems of previous noise reduction efficiency to assess quantitatively and compare them. For this study, skull X-ray imaging is obtained using various kVp and mAs using the skull phantom using the X-ray device of Siemens. In addition, contrast to noise ratio (CNR) and coefficient of variation (COV) are compared and measured when noisy image, median filter, Wiener filter and TV algorithm were applied to each phantom imaging. Experiments showed that when TV algorithms were applied, CNR and COV characteristics were excellent under all conditions. In conclusion, we've been able to see if we can use TV algorithm to improve image quality and CNR could be seen to increase due to the decrease in noise as the amount of increased mAs. On the other hand, COV decreased as the amount of increased mAs, and when kVp increased, noise was reduced and the transmittance was increased, so COV was reduced.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2012.05a
• /
• pp.6-9
• /
• 2012

In this study, we compare two image denoising methods which are the Rudin-Osher-Fatemi total variation (TV) model and the nonlocal means (NLM) algorithm on medical images. To evaluate those methods, we used two well known measuring metrics. The methods are tested with a CT image, one X-Ray image, and three MRI images. Experimental result shows that the NML algorithm can give better results than the ROF TV model, but computational complexity is high.

• Progress in Medical Physics
• /
• v.23 no.4
• /
• pp.252-260
• /
• 2012

The reduction of radiation dose from x-ray is a main concern in computed tomography (CT) imaging due to the side-effect of the dose on human body. Recently, the various methods for dose reduction have been studied in CT and one of the method is a iterative reconstruction based on total variation (TV) minimization at few-views data. In this paper, we evaluated the image quality between total variation (TV) minimization algorithm and Feldkam-Davis-kress (FDK) algorithm in micro computed tomography (CT). To evaluate the effect of TV minimization algorithm, we produced a cylindrical phantom including contrast media, water, air inserts. We can acquire maximum 400 projection views per rotation of the x-ray tube and detector. 20, 50, 90, 180 projection data were chosen for evaluating the level of image restoration by TV minimization. The phantom and mouse image reconstructed with FDK algorithm at 400 projection data used as a reference image for comparing with TV minimization and FDK algorithm at few-views. Contrast-to-noise ratio (CNR), Universal quality index (UQI) were used as a image evaluation metric. When projection data are not insufficient, our results show that the image quality of reconstructed with TV minimization is similar to reconstructed image with FDK at 400 view. In the cylindrical phantom study, the CNR of TV image was 5.86, FDK image was 5.65 and FDK-reference was 5.98 at 90-views. The CNR of TV image 0.21 higher than FDK image CNR at 90-views. UQI of TV image was 0.99 and FDK image was 0.81 at 90-views. where, the number of projection is 90, the UQI of TV image 0.18 higher than FDK image at 90-views. In the mouse study UQI of TV image was 0.91, FDK was 0.83 at 90-views. the UQI of TV image 0.08 higher than FDK image at 90-views. In cylindrical phantom image and mouse image study, TV minimization algorithm shows the best performance in artifact reduction and preserving edges at few view data. Therefore, TV minimization can potentially be expected to reduce patient dose in clinics.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.6 no.2
• /
• pp.9-24
• /
• 2002

Total Variation(TV) regularization method is effective for reconstructing "blocky", discontinuous images from contaminated image with noise. But TV is represented by highly nonlinear integro-differential equation that is hard to solve. There have been much effort to obtain stable and fast methods. C. Vogel introduced "the Fixed Point Lagged Diffusivity Iteration", which solves the nonlinear equation by linearizing. In this paper, we apply multigrid(MG) method for cell centered finite difference (CCFD) to solve system arise at each step of this fixed point iteration. In numerical simulation, we test various images varying noises and regularization parameter $\alpha$ and smoothness $\beta$ which appear in TV method. Numerical tests show that the parameter ${\beta}$ does not affect the solution if it is sufficiently small. We compute optimal $\alpha$ that minimizes the error with respect to $L^2$ norm and $H^1$ norm and compare reconstructed images.

• Journal of Magnetics
• /
• v.21 no.4
• /
• pp.593-598
• /
• 2016

In order to reduce the amount of noise component in X-ray imaging system, various reduction techniques were frequently used in the field of diagnostic imaging. Although the previous techniques -such as median, Wiener filters and Anscombe noise reduction technique - were able to reduce the noise, the edge information was still damaged. In order to cope with this problem, total variation (TV) noise reduction technique has been developed and researched. The purpose of this study was to evaluate and compare the image quality using normalized noise power spectrum (NNPS) and contrast-to-noise ratio (CNR) through simulations and experiments with respect to the above-mentioned noise reduction techniques. As a result, not only lowest NNPS value but also highest CNR values were acquired using a TV noise reduction technique. In conclusion, the results demonstrated that TV noise reduction technique is proved as the most practical method to ensure accurate denoising in X-ray imaging system.

• Journal of the Semiconductor & Display Technology
• /
• v.12 no.2
• /
• pp.27-32
• /
• 2013

Shape From Focus (SFF) is a passive optical technique to recover 3D structure of an object that utilizes focus information from 2D images of the object taken at different focus levels. Mostly, SFF methods use a single focus measure to compute image focus quality of each pixel in the image sequence. However, it is difficult to recover accurate 3D shape using a single focus measure, as different focus measures perform differently in diverse conditions. In this paper, a nonlinear Total Variation (TV) based approach is proposed for 3D shape recovery. To improve the result of surface reconstruction, several initial depth maps are obtained using different focus measures and the resultant 3D shape is obtained by diffusing them through TV. The proposed method is tested and evaluated by using image sequences of synthetic and real objects. The results and comparative analysis demonstrate the effectiveness of our method.

• Journal of the Korean Mathematical Society
• /
• v.55 no.3
• /
• pp.719-734
• /
• 2018

An image restoration problem with Poisson noise arises in many applications of medical imaging, astronomy, and microscopy. To overcome ill-posedness, Total Variation (TV) model is commonly used owing to edge preserving property. Since staircase artifacts are observed in restored smooth regions, higher-order TV regularization is introduced. However, sharpness of edges in the image is also attenuated. To compromise benefits of TV and higher-order TV, the weighted sum of the non-convex TV and non-convex higher order TV is used as a regularizer in the proposed variational model. The proposed model is non-convex and non-smooth, and so it is very challenging to solve the model. We propose an iterative reweighted algorithm with the proximal linearized alternating direction method of multipliers to solve the proposed model and study convergence properties of the algorithm.

• Progress in Medical Physics
• /
• v.27 no.1
• /
• pp.1-7
• /
• 2016

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at $90kV_p$, 6 mAs and a CMOS-type flat-panel detector having a $198-{\mu}m$ pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of ${\theta}=60^{\circ}$ and an angle step of ${\Delta}{\theta}=1.2^{\circ}$ and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

• Journal of IKEEE
• /
• v.20 no.2
• /
• pp.152-162
• /
• 2016

Electrical resistivity tomography (ERT) is a technique to reconstruct the internal resistivity distribution using the measured voltages on the surface electrodes. ERT inverse problem suffers from ill-posedness nature, so regularization methods are used to mitigate ill-posedness. The reconstruction performance varies depending on the type of regularization method. In this paper, an interacting dual-mode regularization method is proposed with two different regularization methods, L1-norm regularization and total variation (TV) regularization, to achieve robust reconstruction performance. The interacting dual-mode regularization method selects the suitable regularization method and combines the regularization methods based on computed mode probabilities depending on the actual conditions. The proposed method is tested with numerical simulations and the results demonstrate an improved reconstruction performance.