Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory bodies. As expected, increasing the degree of compression leads to decreasing image fidelity. The extent of allowable irreversible compression is dependent on the imaging modality and the nature of the image pathology as well as anatomy. Interpolation, which often causes image distortion, has been extensively used to rescale images during radiological diagnosis. This work attempts to assess the quality of medical images after the application of lossy compression followed by rescaling. This research proposes a full-reference objective measure of quality for medical images that considers their deterministic and statistical properties. Statistical features are acquired from the frequency domain of the signal and are combined with elements of the structural similarity index (SSIM). The aim is to construct a model that is specialized for medical images and that could serve as a predictor of quality.
In this paper, we propose a Detail-Preserving Sparse Model (DPSM) for de-noising of images that are usually interfered by noise on the Wireless Multimedia Sensor Network (WMSN). Specifically, based on the Structural SIMilarity (SSIM), the DPSM first incorporates a structural-preserving constraint, which enables the structure in the reconstructed image to be close to the ideal no-noise image. In addition, the DPSM adopts a residual ratio as the stopping condition of the sparse solution algorithm (e.g., Orthogonal Matching Pursuit), which enables the structures to be reconstructed under high noise conditions. The experimental results on several WMSN images have demonstrated the superiority of the proposed DPSM method over several well-known de-noising approaches in terms of PSNR and SSIM.
Due to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image denoising. Experimental results on several test images were taken on Magnetic Resonance Imaging (MRI), X-RAY and Computed Tomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio (PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM), and Correlation Coefficient (CC) were computed. The results shows that Contourlet based Medical image denoising methods are achieving significant improvement in association with Neighsureshrink thresholding technique.
In this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.
-level structural model. The ISM method is the rule and
guidance on the complexity of the relationship between the elements of the system
elements (J. W. Warfield, 1974)
The various steps in the ISM methodology (A. P. Sage, 1977) are as follows:
Step 1. The variables (criteria) are considered for the system under consideration.
Step 2. Based on the variables identified in step 1, a context relationship between the
variables is established to determine which variables are to be examined.
Step 3. The structure of the personalized interaction matrix (SSIM) is developed for
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