Structural Similarity Values Research Articles

StructAnalyzer - a tool for sequence versus structure similarity analysis.

In the world of RNAs and proteins, similarities at the level of primary structures of two comparable molecules usually correspond to structural similarities at the tertiary level. In other words, measures of sequence and structure similarities are in general correlated - a high value of sequence similarity imposes a high value of structural similarity. However, important exceptions that stay in contrast to this general rule can be identified. It is possible to find similar structures with very different sequences, as well as similar sequences with very different structures. In this paper, we focus our attention on the latter case and propose a tool, called StructAnalyzer, supporting analysis of relations between the sequence and structure similarities. Recognition of tertiary structure diversity of molecules with very similar primary structures may be the key for better understanding of mechanisms influencing folding of RNAs or proteins, and as a result for better understanding of their function. StructAnalyzer allows exploration and visualization of structural diversity in relation to sequence similarity. We show how this tool can be used to screen RNA structures in Protein Data Bank (PDB) for sequences with structural variants.

Geodesic Path-Based Diffusion Acceleration for Image Denoising

We propose an advanced anisotropic-diffusion (AD)-based approach for an image denoising method, which utilizes a geodesic path to produce single-pass adaptive smoothing by analyzing the diffusion continuity. The proposed method consists of the following four procedures: element-weight determination, geodesic path-based kernel (GPK) generation, single-pass smoothing using the GPK, and post-processing of the GPK smoothing. In the first procedure, weights for neighboring pixels are calculated by diffusivity analysis. In the second procedure, a geodesic path is selected using a geodesic distance that is calculated by a diffusion continuity analysis. In the third procedure, GPK-based smoothing is applied to a given noisy image to extract the noise-free pixel value. Finally, a distant AD that uses the double diffusion length is applied to the resultant image by the GPK filtering to enhance the quality of noise suppression in smooth regions. In addition to the main procedures, schemes for the robust outlier reduction and complexity reduction are introduced. The simulation results showed that the proposed method improved the denoising quality by increasing the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by up to 4.094 dB and 0.057, respectively, compared to the AD-based benchmark methods. Compared to block-matching and 3-D filtering, the proposed method showed comparable quality of noise reduction with similar PSNR and SSIM values, which it accomplished with much less computation time.

Perceptual Quality-Aware Power Reduction Technique for Organic Light Emitting Diodes

In this paper, a perceptual quality-aware power reduction algorithm is proposed. Structural similarity (SSIM) is a widely used metric to evaluate perceptual image quality between original and processed images in a display. The proposed algorithm linearly changes the curve for the gray-level mapping between the input and output considering the target SSIM value. It then modifies the SSIM equation based on the modified curve and extracts the solution, which is the slope of the curve, from the equation. Finally, the algorithm optimally controls image quality by changing the slope of the gray-level mapping curve. Simulation results demonstrate that the proposed algorithm preserves image quality, regardless of image characteristics. In particular, the average SSIM and peak signal-to-noise-ratio values of the proposed algorithm were up to 0.021 and 11.203 dB higher than those of the benchmark algorithm. In addition, the average computation time of the proposed algorithm was up to 0.619 $\mu$ s per pixel lower than that of the benchmark algorithm.

Efficient 2-D synthetic aperture radar image reconstruction from compressed sampling using a parallel operator splitting structure

This paper investigates an efficient compressed sensing (CS) approach that can be used to reconstruct 2-D millimeter-wave synthetic aperture radar (SAR) images from under-sampled measurements. This approach minimizes a linear combination of four terms corresponding to a least squares data fitting, ℓ1 norm regularization, total variation (TV) and a bounding operator. Although the strong convergence of this approach cannot be guaranteed, this approach always converges to a stable structural similarity (SSIM) value with a combination of a parallel operator splitting structure and a FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) updating stage. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and computation complexity.

Enhancing Medical Images by New Fuzzy Membership Function Median Based Noise Detection and Filtering Technique

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary pair (i.e., the left and right dictionaries) by employing a subspace partition technique on the Grassmann manifold, wherein the refined dictionary pair is obtained through a sub-dictionary pair merging. The DPLG obtains a sparse representation by encoding each image patch only with the selected sub-dictionary pair. The non-zero elements of the sparse representation are further smoothed by the graph Laplacian operator to remove the noise. Consequently, the DPLG algorithm not only preserves the inherent 2D geometric structure of natural images but also performs manifold smoothing in the 2D sparse coding space. We demonstrate that the DPLG algorithm also improves the structural SIMilarity values of the perceptual visual quality for denoised images using the experimental evaluations on the benchmark images and Berkeley segmentation data sets. Moreover, the DPLG also produces the competitive peak signal-to-noise ratio values from popular image denoising algorithms.

Feasibility of Automated Detection of Inter-fractional Deviation in Patient Positioning Using Structural Similarity Index: Preliminary Results

†The modern radiotherapy technique which delivers a large amount of dose to patients asks to confirm the positions of patients or tumors more accurately by using X-ray projection images of high-definition. However, a rapid increase in patient’s exposure and image information for CT image acquisition may be additional burden on the patient. In this study, by introducing structural similarity (SSIM) index that can effectively extract the structural information of the image, we analyze the differences between daily acquired x-ray images of a patient to verify the accuracy of patient positioning. First, for simulating a moving target, the spherical computational phantoms changing the sizes and positions were created to acquire projected images. Differences between the images were automatically detected and analyzed by extracting their SSIM values. In addition, as a clinical test, differences between daily acquired x-ray images of a patient for 12 days were detected in the same way. As a result, we confirmed that the SSIM index was changed in the range of 0.85∼1 (0.006∼1 when a region of interest (ROI) was applied) as the sizes or positions of the phantom changed. The SSIM was more sensitive to the change of the phantom when the ROI was limited to the phantom itself. In the clinical test, the daily change of patient positions was 0.799∼0.853 in SSIM values, those well described differences among images. Therefore, we expect that SSIM index can provide an objective and quantitative technique to verify the patient position using simple x-ray images, instead of time and cost intensive three-dimensional x-ray images.

Computed tomography images de-noising using a novel two stage adaptive algorithm

In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

Document image super‐resolution using structural similarity and Markov random field

Low-resolution (LR) document images may cause difficulties in reading or low recognition rates in computer vision. Thus, it is necessary to improve the resolution of an LR document image via some algorithms. In this study, a novel document image super-resolution (SR) method using structural similarity and Markov random field (MRF) is proposed. First, the non-local algorithm is utilised to find similar patches. Instead of using the Euclidian distance, a modified chi-square distance is proposed to measure the patch similarity because the bimodality characteristic of the document images can be better described by this modified chi-square distance. Finally, the structural similarity of similar patches is served as a constraint for the MRF-based SR method, which is proper to describe the neighbouring relationship between patches. The SR reconstruction for LR images of printed and handwritten documents are carried out by the proposed algorithm. Experimental results show that the reconstructed SR images obtain higher peak signal-to-noise ratio and structural similarity values than those of several state-of-the-art SR methods and visually pleasant SR images can be produced as well.

Quantitative analysis of seismic velocity tomography in rock burst hazard assessment

In order to quantitatively evaluate the relationship between the tomographic images of P wave velocity and rock burst hazard, the seismic velocity tomography was used to generate the P wave velocity tomograms during the retreat of a longwall panel in a coal mine. Subsequently, a novel index (bursting strain energy) was proposed to characterize the mining seismic hazard map. Finally, the structural similarity (SSIM) index in the discipline of image quality assessment was introduced to quantitatively assess the relation between the bursting strain energy index images and the tomographic images of P wave velocity. The results show that the bursting strain energy index is appropriate for quantitative analysis and seems to be better for expressing the mining seismic hazard than the conventional map. The SSIM values of the future bursting strain energy compared with the P wave velocity and the current bursting strain energy reach up to 0.8908 and 0.8462, respectively, which illustrate that the P wave velocity and the bursting strain energy both are able to detect the rock burst hazard region. Specifically, seismic velocity tomography is superior to the bursting strain energy index in the detection range and the precision and accuracy of detection results.

Adaptive multimedia streaming using a simulation test bed

The major purpose of this paper is to transmit an existing video above a complete simulated video streaming architecture based on Network Simulator (NS2). Thanks to our architecture, the visual quality evaluation of the distributed streaming platform under various conditions is simplified. Indeed, the received video can be easily visualized using a classic video client or be compared using the Peak Signal-Noise Ratio (PSNR) value and the Structural SIMilarity (SSIM) value. In the case study, we compare adaptive video congestion strategies using a transcoder, Datagram Congestion Control Protocol (DCCP) and TCP-Friendly Rate Control (TFRC).

Adaptive Wavelet Packet-Based De-speckling of Ultrasound Images with Bilateral Filter

A new adaptive wavelet packet-based approach to minimize speckle noise in ultrasound images is proposed. This method combines wavelet packet thresholding with a bilateral filter. Here, the best bases after wavelet packet decomposition are selected by comparing the first singular value of all sub-bands, and the noisy coefficients are thresholded using a modified NeighShrink technique. The algorithm is tested with various ultrasound images, and the results, in terms of peak signal-to-noise ratio and mean structural similarity values, are compared with those for some well-known de-speckling techniques. The simulation results indicate that the proposed method has better potential to minimize speckle noise and retain fine details of the ultrasound image.

An Object-Oriented Simulator for 3D Digital Breast Tomosynthesis Imaging System

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values.

Fast super-resolution algorithms using one-dimensional patch-based training and directional interpolation

This study proposes fast super-resolution algorithms to up-scale an input low-resolution image into a high-resolution image. Conventional learning-based super-resolution algorithms require large memory space to store a huge amount of synthesis information, and they require significant computation because of the large number of two-dimensional matching operations. To mitigate this problem, the authors train a dictionary using one-dimensional patch-based training and K-means clustering at the learning phase, and they use one-dimensional matching and interpolation based on the trained dictionary at the synthesis phase. Such one-dimensional content-adaptive interpolation is applied separately in horizontal and vertical directions. In addition, the authors propose a hybrid algorithm in which directional interpolation is utilised for vertical interpolation to further reduce the dictionary size and the so called staircase artefact. Simulation results show that the proposed algorithm has higher peak-to-peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) values while providing significantly smaller dictionary size and faster computation than the latest learning-based super-resolution algorithm.

The quest for the ancestor of the Ciliophora: a brief review of the continuing problem

Although the assumption has long been made that the Ciliophora arose from flagellates, limited progress has been made in determining exactly what extant group - flagellate or other - may best resemble the ancestral “preciliate” assemblage. The distinctiveness of the ciliates and of the many protist phyla containing flagellated stages in their life cycles makes the recognition of potentially homologous features difficult. The suggestion that there may be a phylogenetic relationship between dinoflagellates and ciliates, while seeming unlikely from consideration of a number of specialized characters, is attracting increased attention. This is because some basic similarities (possibly shared derived characters) are characteristic of many species from both groups: cortical alveoli, tubular mitochondrial cristae, kinetidal systems, acentric mitoses with persisting nuclear envelope, functional cytostome, extrusomes (mucocysts and explosive trichocysts), locomotory organelles, and small subunit rRNAs (close structural similarity values). But many questions remain unanalyzed or unanswered, and caution is still advisable in drawing any firm conclusions about the evolutionary closeness of ciliates and dinoflagellates.