This paper introduces a new method of combining total variation denoising (TVD) and median filtering to reduce noise in optical coherence tomography (OCT) image volumes. Both noise from image acquisition and digital processing severely degrade the quality of the OCT volumes. The OCT volume consists of the anatomical structures of interest and speckle noise. For denoising purposes we model speckle noise as a combination of additive white Gaussian noise (AWGN) and sparse salt and pepper noise. The proposed method recovers the anatomical structures of interest by using a Median filter to remove the sparse salt and pepper noise and by using TVD to remove the AWGN while preserving the edges in the image. The proposed method reduces noise without much loss in structural detail. When compared to other leading methods, our method produces similar results significantly faster.

Total Variation Denoising for Optical Coherence Tomography

The mathematical approach calculus of variation is commonly used to find an unknown function that minimizes or maximizes the functional. Retrieving the original image from the degraded one, such problems are called inverse problems. The most basic example for inverse problem is image denoising. Variational methods are formulated as optimization problems and provides a good solution to image denoising. Three such variational methods Tikhonov model, ROF model and Total Variation-L1 model for image denoising are studied and implemented. Performance of these variational algorithms are analyzed for different values of regularization parameter. It is found that small value of regularization parameter causes better noise removal whereas large value of regularization parameter preserves well sharp edges. The Euler’s Lagrangian equation corresponding to an energy functional used in variational methods is solved using gradient descent method and the resulting partial differential equation is solved using Euler’s forward finite difference method. The quality metrics are computed and the results are compared in this paper.

/pdf/performance-comparison-of-total-variation-based-image-1up9bhrlrv.pdf

Performance Comparison of Total Variation based Image Regularization Algorithms

Noise reduction is a very important task in image processing. In this aim, many approaches and methods have been developed and proposed in the literature. In this paper, we present a new restoration method for noisy images by minimizing the Total Variation (TV) under constraints using a multilayer neural network (MLP). Indeed, the obtained Euler-Lagrange functional is resolved by minimizing an error functional. The MLP parameters (weights) in this case are adjusted to minimize appropriate functional and provides optimal solution. The proposed method can restore degraded images and preserves the discontinuities. The effectiveness of our a pproach has been tested on synthetic and real images, and compared with known restoration methods

/pdf/image-restoration-using-multilayer-neural-networks-with-1wcq8mp2bx.pdf

Image Restoration using Multilayer Neural Networks with Minimization of Total Variation Approach

In this paper, a new denoising technique for images corrupted with additive salt and pepper noise and white Gaussian noise is proposed. The technique used here is to combine the anisotropic diffusion (PM) model and total variation (TV) model. The new technique utilizes both advantages of PM model and TV model, while avoiding the disadvantages of both of them. To evaluate our algorithm several experiments have been conducted. The experimental results affirm the high performance of our model.

http://www.ijcsi.org/papers/IJCSI-10-3-2-37-43.pdf

A Model of Image Denoising Based on the Fusion of Anisotropic Diffusion and Total Variation Models

Computer Tomography in medical imaging provides human internal body pictures in the digital form. The more quality images it provides, the better information we get. Normally, medical imaging can be constructed by projection data from several perspectives. In this paper, our research challenges and describes a numerical method for refining the image of a Region of Interest (ROI) by constructing support within a standard CT image. It is obvious that the quality of tomographic slice is affected by artifacts. CT using filter and K-means clustering provides a way to reconstruct an ROI with minimal artifacts and improve the degree of the spatial resolution. Experimental results are presented for improving the reconstructed images, showing that the approach enhances the overall resolution and contrast of ROI images. Our method provides a number of advantages: robustness with noise in projection data and support construction without the need to acquire any additional setup.

/pdf/a-support-construction-for-ct-image-based-on-k-means-1b0d0a38e2.pdf

A Support Construction for CT Image Based on K-Means Clustering

In this paper, we study signal denoising technique based on total variation (TV) which was reported by Ivan W. Selesnick, Ilker Bayram [1]. Here, we present a directional total variation algorithm for image denoising. In most of the image denoising methods, the total variation denoising is directly performed on the noisy images. In this work, we apply a 1D TV denoising algorithm in sequential manner on the pixel sequence obtained in different orientations including zig-zag, horizontal and vertical. The performance of the proposed method is evaluated using the standard test images and the quality of the denoised images is assessed using various objective metrics such as peak signal to noise ratio (PSNR), structural similarity index metrics (SSIM), visual signal to noise ratio (VSNR). Various experiments show that the proposed method provides promising results with less computational load.

/pdf/directional-total-variation-filtering-based-image-denoising-27re74vm7g.pdf

Directional Total Variation Filtering Based Image Denoising Method

The advent of IoT and blockchain had led to its subsequent proliferation in the medical industry. IoT and its devices are exclusively accompanied by its security threats such as its access to extremely sensitive data while establishing security and confidentiality as top concerns; this can be anonymized by incorporating blockchain. Owing to the trust-building features of blockchain, it may encourage the enhanced and broad adoption of IoT devices into the healthcare industry. The surrounding robust data protection and integrity are pivotal keys to help secure these devices, enabling the respective personnel to have a command over who uses this data and its purpose. The records in IoT devices are more susceptible to chances of identity thefts, financial data crimes, and spamming as patients don’t own any control over their records/data. The availability of acutely handy IoT devices had still not let patients collect, analyze, secure, and exchange healthcare information seamlessly. Adopting a blockchain and IoT clustered solution can help accomplish a smooth, transparent, economically efficient, and easily operable system combined with secure payment options.

The Desiderata of Blockchain and IoT in Medical and Pharmaceutical Enterprises

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Directional Total Variation Filtering Based Image Denoising Method

The Desiderata of Blockchain and IoT in Medical and Pharmaceutical Enterprises