Median-modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2-DE image processing

Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise-power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.

Myocardial Perfusion Single-Photon Emission Computed Tomography (SPECT) Image Denoising: A Comparative Study

The present study aimed to evaluate the effectiveness of different filters in improving the quality of myocardial perfusion single-photon emission computed tomography (SPECT) images. Data were collected using the Siemens Symbia T2 dual-head SPECT/Computed tomography (CT) scanner. Our dataset included more than 900 images from 30 patients. The quality of the SPECT was evaluated after applying filters such as the Butterworth, Hamming, Gaussian, Wiener, and median-modified Wiener filters with different kernel sizes, by calculating indicators such as the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and contrast-to-noise ratio (CNR). SNR and CNR were highest with the Wiener filter with a kernel size of 5 × 5. Additionally, the Gaussian filter achieved the highest PSNR. The results revealed that the Wiener filter, with a kernel size of 5 × 5, outperformed the other filters for denoising images of our dataset. The novelty of this study includes comparison of different filters to improve the quality of myocardial perfusion SPECT. As far as we know, this is the first study to compare the mentioned filters on myocardial perfusion SPECT images, using our datasets with specific noise structures and mentioning all the elements necessary for its presentation within one document.

Evaluation of denoising techniques to remove speckle and Gaussian noise from dermoscopy images

Computerized methods provide analyses of skin lesions from dermoscopy images automatically. However, the images acquired from dermoscopy devices are noisy and cause low accuracy in automated methods. Therefore, various methods have been applied for denoising in the literature. There are some review-type papers about these methods. However, their authors have focused on either denoising with a specific approach or denoising from other images rather than dermoscopy images, which have a different characteristic. It is not possible to determine which method is the most suitable for denoising from dermoscopy images according to the results presented in them. Therefore, a review on the denoising approaches applied with dermoscopy images is required and, according to our knowledge, there is no such a review-type paper. To fill this gap in the literature, the required review has been performed in this work. Also, in this work, the methods in the literature have been implemented using the same data sets containing images with speckle or Gaussian types of noise. The results have been analyzed not only visually but also quantitatively to compare capabilities of the techniques. Our experiments indicated that each denoising technique has its own disadvantages and advantages. The main contributions of this paper are three-fold: (i) A comprehensive review on the denoising approaches applied with dermoscopy images has been presented. (ii) The denoising techniques have been implemented with the same images for meaningful comparisons. (iii) Both visual and quantitative analyses with different metrics have been performed and comparative performance evaluations have been presented.

Comparing Different Algorithms for the Pseudo-Coloring of Myocardial Perfusion Single-Photon Emission Computed Tomography Images

Single-photon emission computed tomography (SPECT) images can significantly help physicians in diagnosing patients with coronary artery or suspected coronary artery diseases. However, these images are grayscale with qualities that are not readily visible. The objective of this study was to evaluate the effectiveness of different pseudo-coloring algorithms of myocardial perfusion SPECT images. Data were collected using a Siemens Symbia T2 dual-head SPECT/computed tomography (CT) scanner. After pseudo-coloring, the images were assessed both qualitatively and quantitatively. The qualities of different pseudo-color images were examined by three experts, while the images were evaluated quantitatively by obtaining indices such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), normalized color difference (NCD), and structure similarity index metric (SSIM). The qualitative evaluation demonstrated that the warm color map (WCM), followed by the jet color map, outperformed the remaining algorithms in terms of revealing the non-visible qualities of the images. Furthermore, the quantitative evaluation results demonstrated that the WCM had the highest PSNR and SSIM but the lowest MSE. Overall, the WCM could outperform the other color maps both qualitatively and quantitatively. The novelty of this study includes comparing different pseudo-coloring methods to improve the quality of myocardial perfusion SPECT images and utilizing our collected datasets.

Two-Dimensional Gel Electrophoresis Image Analysis

Gel-based proteomics is still quite widespread due to its high-resolution power; the experimental approach is based on differential analysis, where groups of samples (e.g., control vs diseased) are compared to identify panels of potential biomarkers. However, the reliability of the result of the differential analysis is deeply influenced by 2D-PAGE maps image analysis procedures. The analysis of 2D-PAGE images consists of several steps, such as image preprocessing, spot detection and quantitation, image warping and alignment, spot matching. Several approaches are present in literature, and classical or last-generation commercial software packages exploit different algorithms for each step of the analysis. Here, the most widespread approaches and a comparison of the different strategies are presented.

A Slice-based 13C-detected NMR Spin System Forming and Resonance Assignment Method

Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, ¹H-detected experiments are the dominant NMR technique used due to the high sensitivity of ¹H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of ¹³C nuclei. Consequently, ¹³C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for ¹³C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two ¹³C-detected spectra, CBCACON, and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of 'Ubiquitin' and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92 percent correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86 percent and an average precision of 88 percent. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure.

2D-DIGE and Fluorescence Image Analysis

2D-DIGE is still a very widespread technique in proteomics for the identification of panels of biomarkers, allowing to tackle with some important drawback of classical two-dimensional gel-electrophoresis. However, once 2D-gels are obtained, they must undergo a quite articulated multistep image analysis procedure before the final differential analysis via statistical mono- and multivariate methods. Here, the main steps of image analysis software are described and the most recent procedures reported in the literature are briefly presented.

MatCol: a tool to measure fluorescence signal colocalisation in biological systems

Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student's t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R2 = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.

Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection

2-D gel electrophoresis is the most commonly used method in biomedicine to separate even thousands of proteins in a complex sample on a single gel. Even though the technique is quite known, there is still a need to find an efficient and reliable method for detection of protein spots on gel image. In this paper, a three-step algorithm based on mixture of 2-D normal distribution functions is introduced to improve the efficiency of spot detection performed by the existing algorithms, namely Pinnacle software and watershed segmentation method. Comparison of methods is based on using simulated and real data sets with known true spot positions and different number of spots. Fitting a mixture of components to gel image allows for achieving higher sensitivity in detecting spots, regardless the method used to find initial conditions for the model parameters, and it leads to better overall performance of spot detection. By using mixture model, location of spot centers can be estimated with higher accuracy than using the Pinnacle method. An application of spot shape modeling gives higher sensitivity of obtaining low-intensity spots than the watershed method, which is crucial in the discovery of novel biomarkers.

Image Pretreatment Tools I: Algorithms for Map Denoising and Background Subtraction Methods

One of the critical steps in two-dimensional electrophoresis (2-DE) image pre-processing is the denoising, that might aggressively affect either spot detection or pixel-based methods. The Median Modified Wiener Filter (MMWF), a new nonlinear adaptive spatial filter, resulted to be a good denoising approach to use in practice with 2-DE. MMWF is suitable for global denoising, and contemporary for the removal of spikes and Gaussian noise, being its best setting invariant on the type of noise. The second critical step rises because of the fact that 2-DE gel images may contain high levels of background, generated by the laboratory experimental procedures, that must be subtracted for accurate measurements of the proteomic optical density signals. Here we discuss an efficient mathematical method for background estimation, that is suitable to work even before the 2-DE image spot detection, and it is based on the 3D mathematical morphology (3DMM) theory.

Comparative Evaluation of Software Features and Performances

Analysis of two-dimensional gel images is a crucial step for the determination of changes in the protein expression, but at present, it still represents one of the bottlenecks in 2-DE studies. Over the years, different commercial and academic software packages have been developed for the analysis of 2-DE images. Each of these shows different advantageous characteristics in terms of quality of analysis. In this chapter, the characteristics of the different commercial software packages are compared in order to evaluate their main features and performances.

Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data

Dimensionality reduction is largely and successfully employed for the visualization and discrimination of patterns, hidden in multidimensional proteomics datasets. Principal component analysis (PCA), which is the preferred approach for linear dimensionality reduction, may present serious limitations, in particular when samples are nonlinearly related, as often occurs in several two-dimensional electrophoresis (2-DE) datasets. An aggravating factor is that PCA robustness is impaired when the number of samples is small in comparison to the number of proteomic features, and this is the case in high-dimensional proteomic datasets, including 2-DE ones. Here, we describe the use of a nonlinear unsupervised learning machine for dimensionality reduction called minimum curvilinear embedding (MCE) that was successfully applied to different biological samples datasets. In particular, we provide an example where we directly compare MCE performance with that of PCA in disclosing neuropathic pain patterns, hidden in a multidimensional proteomic dataset.

Comparative Proteomics for the Evaluation of Protein Expression and Modifications in Neurodegenerative Diseases

Together with hypothesis-driven approaches, high-throughput differential proteomic analysis performed primarily not only in human cerebrospinal fluid and serum but also on protein content of other tissues (blood cells, muscles, peripheral nerves, etc.) has been used in the last years to investigate neurodegenerative diseases. Even if the goal for these analyses was mainly the discovery of neurodegenerative disorders biomarkers, the characterization of specific posttranslational modifications (PTMs) and the differential protein expression resulted in being very informative to better define the pathological mechanisms. In this chapter are presented and discussed the positive aspects and challenges of the outcomes of some of our investigations on neurological and neurodegenerative disease, in order to highlight the important role of protein PTMs studies in proteomics-based approaches.

Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

MOTIVATION

Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.

METHODS

Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.

RESULTS

We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.

CONCLUSION

Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules.

AVAILABILITY

https://sites.google.com/site/carlovittoriocannistraci/home.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A novel Gaussian extrapolation approach for 2D gel electrophoresis saturated protein spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification.

Identification and Predictive Value of Interleukin-6 + Interleukin-10 + and Interleukin-6 − Interleukin-10 + Cytokine Patterns in ST-Elevation Acute Myocardial Infarction

Rationale:

At the onset of ST-elevation acute myocardial infarction (STEMI), patients can present with very high circulating interleukin-6 (IL-6 + ) levels or very low-IL-6 – levels.

Objective:

We compared these 2 groups of patients to understand whether it is possible to define specific STEMI phenotypes associated with outcome based on the cytokine response.

Methods and Results:

We compared 109 patients with STEMI in the top IL-6 level (median, 15.6 pg/mL; IL-6 + STEMI) with 96 in the bottom IL-6 level (median, 1.7 pg/mL; IL-6 − STEMI) and 103 matched controls extracted from the multiethnic First Acute Myocardial Infarction study. We found minimal clinical differences between IL-6 + STEMI and IL-6 − STEMI. We assessed the inflammatory profiles of the 2 STEMI groups and the controls by measuring 18 cytokines in blood samples. We exploited clustering analysis algorithms to infer the functional modules of interacting cytokines. IL-6 + STEMI patients were characterized by the activation of 2 modules of interacting signals comprising IL-10, IL-8, macrophage inflammatory protein-1α, and C-reactive protein, and monocyte chemoattractant protein-1, macrophage inflammatory protein-1β, and monokine induced by interferon-γ. IL-10 was increased both in IL-6 + STEMI and IL-6 − STEMI patients compared with controls. IL-6 + IL-10 + STEMI patients had an increased risk of systolic dysfunction at discharge and an increased risk of death at 6 months in comparison with IL-6 − IL-10 + STEMI patients. We combined IL-10 and monokine induced by interferon-γ (derived from the 2 identified cytokine modules) with IL-6 in a formula yielding a risk index that outperformed any single cytokine in the prediction of systolic dysfunction and death.

Conclusions:

We have identified a characteristic circulating inflammatory cytokine pattern in STEMI patients, which is not related to the extent of myocardial damage. The simultaneous elevation of IL-6 and IL-10 levels distinguishes STEMI patients with worse clinical outcomes from other STEMI patients. These observations could have potential implications for risk-oriented patient stratification and immune-modulating therapies.

Ceruloplasmin oxidation, a feature of Parkinson's disease CSF, inhibits ferroxidase activity and promotes cellular iron retention

Parkinson's disease is a neurodegenerative disorder characterized by oxidative stress and CNS iron deposition. Ceruloplasmin is an extracellular ferroxidase that regulates cellular iron loading and export, and hence protects tissues from oxidative damage. Using two-dimensional electrophoresis, we investigated ceruloplasmin patterns in the CSF of human Parkinson's disease patients. Parkinson's disease ceruloplasmin profiles proved more acidic than those found in healthy controls and in other human neurological diseases (peripheral neuropathies, amyotrophic lateral sclerosis, and Alzheimer's disease); degrees of acidity correlated with patients' pathological grading. Applying an unsupervised pattern recognition procedure to the two-dimensional electrophoresis images, we identified representative pathological clusters. In vitro oxidation of CSF in two-dimensional electrophoresis generated a ceruloplasmin shift resembling that observed in Parkinson's disease and co-occurred with an increase in protein carbonylation. Likewise, increased protein carbonylation was observed in Parkinson's disease CSF, and the same modification was directly identified in these samples on ceruloplasmin. These results indicate that ceruloplasmin oxidation contributes to pattern modification in Parkinson's disease. From the functional point of view, ceruloplasmin oxidation caused a decrease in ferroxidase activity, which in turn promotes intracellular iron retention in neuronal cell lines as well as in primary neurons, which are more sensitive to iron accumulation. Accordingly, the presence of oxidized ceruloplasmin in Parkinson's disease CSF might be used as a marker for oxidative damage and might provide new insights into the underlying pathological mechanisms.

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

MOTIVATION

Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space.

METHODS

'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering.

RESULTS

Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin.

CONCLUSION

MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal.

AVAILABILITY

https://sites.google.com/site/carlovittoriocannistraci/home.