Tetrolet Coefficients Research Articles

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

AbstractMedical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low‐ and high‐pass tetrolet coefficients. In the second module, multiple low‐pass tetrolet coefficients are applied through a modified transform‐based gamma correction method. Generalized cross‐validation is used on multiple high‐pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon‐based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal‐to‐noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.

CT image denoising using locally adaptive shrinkage rule in tetrolet domain

In Computed Tomography (CT), image degradation such as noise and detail blurring is one of the universal problems due to hardware restrictions. The problem of noise in CT images can be solved by image denoising. The main aim of image denoising is to reduce the noise as well as preserve the important features such as edges, corners, textures and sharp structures. Due to the large capability of noise suppression in noisy signals according to neighborhood pixels or coefficients, this paper presents a new technique to denoise CT images with edge preservation in tetrolet domain (Haar-type wavelet transform) where a locally adaptive shrinkage rule is performed on high frequency tetrolet coefficients in such a way that noise can be reduced more effectively. The experimental results of the proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with some standard existing methods where it is observed that performance of the proposed scheme is superior to the existing methods in terms of visual quality, MSE, PSNR and Image Quality Index (IQI).

Research on Polarization Image Fusion Using Tetrolet Transform

Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. Firstly, the magnitude of polarization image and angle of polarization image can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using tetrolet transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region energy information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused tetrolet coefficients. Experimental results show that the proposed method can detect image features more effectively and the fused image has better subjective visual effect.

A Reduced Reference Image Quality Measure Using Bessel K Forms Model for Tetrolet Coefficients

In this paper, we introduce a Reduced Reference Image Quality Assessment (RRIQA) measure based on the natural image statistic approach. A new adaptive transform called Tetrolet is applied to both reference and distorted images. To model the marginal distribution of tetrolet coefficients Bessel K Forms (BKF) density is proposed. Estimating the parameters of this distribution allows to summarize the reference image with a small amount of side information. Five distortion measures based on the BKF parameters of the original and processed image are used to predict quality scores. A comparison between these measures is presented showing a good consistency with human judgment.

Tetrolet shrinkage with anisotropic total variation minimization for image approximation

In this paper, an anisotropic total variation (ATV) minimization is combined with the new adaptive tetrolet transform for discontinuity-preserving image processing. In order to suppress the pseudo-Gibbs artefacts and to increase the regularity, the conventional shrinkage results are further processed by a total variation (TV) minimization scheme, in which only the insignificant tetrolet coefficients of the image are changed by the use of ATV constrained projection, instead of previous TV projections. Numerical experiments of piecewise-smooth images show the good performance of the proposed hybrid method to recover the shape of edges and important detailed directional components, in comparison to some existing methods.