Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Abstract

Medical image denoising is the main step in medical diagnosis, which removes the noise without affecting relevant features of the image. There are many algorithms that can be used to reduce the noise such as: threshold and the sparse representation. The K-SVD is one of the most popular sparse representation algorithms, which is depend on Orthogonal Matching Pursuit (OMP) and Discrete Cosine Transform (DCT) dictionary. In this paper, an algorithm for image denoising was designed to develop K-SVD by using Regularized Orthogonal Matching Pursuit (ROMP) over log Gabor wavelet adaptive dictionary. To evaluate the performance of the proposed techniques, the results were compared with the results of DCT and Gabor wavelet dictionary. The numerical results show that the performance of our algorithm is more efficient in medical image denoising. KeywordsSparse representation (SR), K-SVD, log-Gabor wavelet dictionary, regularized orthogonal matching pursuit and orthogonal matching pursuit.

Biomedical images enhancement based on swarm optimization and differential evolution technique

“…The method was tested in several images; five of them were selected to illustrate the results. These images present dimensions of 128 × 128, 64 × 64, 128 × 128, 64 × 64, 128 × 128, and 64 × 64 pixels, respectively Farouk et al (2016), and they were corrupted with additive Gaussian noise N(0, σ 2 ), where σ 2 is the estimated noise deviation with noise levels σ = 10, 20, 30, 40, 50, 60, 70, 80 and 90. The first image (MRI image) was used with different resolution128 × 128 and 64 × 64.…”

“…The Structural Similarity Index (SSIM) is used to measure image quality. SSIM is based on the computation of three terms, the luminance term ( ), the contrast term( ), and the structural term ( ) (Farouk et al, 2016). The overall index is a multiplicative combination of the three terms.…”

“…The population size of proposed algorithm is N=60 and a number of parametersN=60. In DMS-PSO and standard PSO, the Linear Decreasing Weight (LDW) (Farouk et al, 2016) is used. In LDW, weight is updated as generation and it can be computed using the following formula: where shows maximum generation.…”

“…Noise suppression can introduce artifacts or cause image blurring, which makes image denoising a complicated task. There are different methods have been proposed to remove noise from images; however, each one examines specific facts of the problem (Farouk et al, 2016).…”

“…It works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data. The K-SVD improved with different type of dictionary to form atoms of it such as: DCT (Elad and Aharon, 2006), Gabor Wavelet (Khedr et al, 2012), Log-Gabor and Log Gabor Wavelet (Farouk et al, 2016). In Ruiz-Reyes et al (2005), the space-alternating generalized expectationmaximization (SAGE) algorithm is used to estimate the values of parameter vector and minimum description length.…”