A Comparative Study on Thresholding Methods in Wavelet-based Image Denoising

AbstractWavelet-based image denoising is an important technique in the area of image noise reduction. Wavelets have their natural ability to represent images in a very sparse form which is the foundation of wavelet-based denoising through thresholding. This paper explores properties of several representative thresholding techniques in wavelets denoising, such as VisuShrink, SureShrink, BayesShrink and Feature Adaptive Shrinkage. A quantitative comparison between these techniques through PSNR (Peak Signal-to-Noise Ratio) is also given

Nonsmooth Adaptive Control Design for a Large Class of Uncertain High-Order Stochastic Nonlinear Systems

This paper investigates the problem of the global stabilization via partial-state feedback and adaptive technique for a class of high-order stochastic nonlinear systems with more uncertainties/unknowns and stochastic zero dynamics. First of all, two stochastic stability concepts are slightly extended to allow the systems with more than one solution. To solve the problem, a lot of substantial technical difficulties should be overcome since the presence of severe uncertainties/unknowns, unmeasurable zero dynamics, and stochastic noise. By introducing the suitable adaptive updated law for an unknown design parameter and appropriate control Lyapunov function, and by using the method of adding a power integrator, an adaptive continuous (nonsmooth) partial-state feedback controller without overparameterization is successfully designed, which guarantees that the closed-loop states are bounded and the original system states eventually converge to zero, both with probability one. A simulation example is provided to illustrate the effectiveness of the proposed approach

Query-Level Stability of Ranking SVM for Replacement Case

AbstractThe quality of ranking determines the success or failure of information retrieval and the goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focus on stability and generalization ability of ranking SVM for replacement case. The query-level stability of ranking SVM for replacement case and the generalization bounds for such ranking algorithm via query-level stability by changing one element in sample set are given

Cascade of classifier ensembles for reliable medical image classification

Medical image analysis and recognition is one of the most important tools in modern medicine. Different types of imaging technologies such as X-ray, ultrasonography, biopsy, computed tomography and optical coherence tomography have been widely used in clinical diagnosis for various kinds of diseases. However, in clinical applications, it is usually time consuming to examine an image manually. Moreover, there is always a subjective element related to the pathological examination of an image. This produces the potential risk of a doctor to make a wrong decision. Therefore, an automated technique will provide valuable assistance for physicians. By utilizing techniques from machine learning and image analysis, this thesis aims to construct reliable diagnostic models for medical image data so as to reduce the problems faced by medical experts in image examination. Through supervised learning of the image data, the diagnostic model can be constructed automatically. The process of image examination by human experts is very difficult to simulate, as the knowledge of medical experts is often fuzzy and not easy to be quantified. Therefore, the problem of automatic diagnosis based on images is usually converted to the problem of image classification. For the image classification tasks, using a single classifier is often hard to capture all aspects of image data distributions. Therefore, in this thesis, a classifier ensemble based on random subspace method is proposed to classify microscopic images. The multi-layer perceptrons are used as the base classifiers in the ensemble. Three types of feature extraction methods are selected for microscopic image description. The proposed method was evaluated on two microscopic image sets and showed promising results compared with the state-of-art results. In order to address the classification reliability in biomedical image classification problems, a novel cascade classification system is designed. Two random subspace based classifier ensembles are serially connected in the proposed system. In the first stage of the cascade system, an ensemble of support vector machines are used as the base classifiers. The second stage consists of a neural network classifier ensemble. Using the reject option, the images whose classification results cannot achieve the predefined rejection threshold at the current stage will be passed to the next stage for further consideration. The proposed cascade system was evaluated on a breast cancer biopsy image set and two UCI machine learning datasets, the experimental results showed that the proposed method can achieve high classification reliability and accuracy with small rejection rate. Many computer aided diagnosis systems face the problem of imbalance data. The datasets used for diagnosis are often imbalanced as the number of normal cases is usually larger than the number of the disease cases. Classifiers that generalize over the data are not the most appropriate choice in such an imbalanced situation. To tackle this problem, a novel one-class classifier ensemble is proposed. The Kernel Principle Components are selected as the base classifiers in the ensemble; the base classifiers are trained by different types of image features respectively and then combined using a product combining rule. The proposed one-class classifier ensemble is also embedded into the cascade scheme to improve classification reliability and accuracy. The proposed method was evaluated on two medical image sets. Favorable results were obtained comparing with the state-of-art results

Gene- or region-based association study via kernel principal component analysis.

BACKGROUND: In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits). Kernel principal component analysis combined with logistic regression test (KPCA-LRT) has been successfully used in classifying gene expression data. Nevertheless, the purpose of association study is to detect the correlation between genetic variations and disease rather than to classify the sample, and the genomic data is categorical rather than numerical. Recently, although the kernel-based logistic regression model in association study has been proposed by projecting the nonlinear original SNPs data into a linear feature space, it is still impacted by multicolinearity between the projections, which may lead to loss of power. We, therefore, proposed a KPCA-LRT model to avoid the multicolinearity. RESULTS: Simulation results showed that KPCA-LRT was always more powerful than principal component analysis combined with logistic regression test (PCA-LRT) at different sample sizes, different significant levels and different relative risks, especially at the genewide level (1E-5) and lower relative risks (RR = 1.2, 1.3). Application to the four gene regions of rheumatoid arthritis (RA) data from Genetic Analysis Workshop16 (GAW16) indicated that KPCA-LRT had better performance than single-locus test and PCA-LRT. CONCLUSIONS: KPCA-LRT is a valid and powerful gene- or region-based method for the analysis of GWAS data set, especially under lower relative risks and lower significant levels.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet

Monitoring the ecological environment of open-pit coalfields in cold zone of Northeast China using Landsat time series images of 2000-2015

Procjena pogoršanja ekološkog okruženja i vegetacije rudnika u Kini zbog prekomjernog vađenja ugljena važna je zbog osjetljivog ekološkog okruženja i niske temperature u hladnim i sušnim područjima. U ovom se istraživanju kao primjeri uzimaju rudnici Haizhou, Gulianhe i Huolinhe s otvorenim jamskim oknom te se predlaže metoda za procjenu njihovog ekološkog okruženja primjenom Landsat vremenske serije slika na temelju varijacija Normalized Difference Vegetation Index-a (NDVI) rudnika s otvorenim jamskim oknom u hladnim i sušnim područjima. Prosječna NDVI vrijednost rudarskog područja izračunavala se svakog mjeseca primjenom podataka Landsat serije slika od 2000 do 2015. Područje nalazišta ugljena pod vegetacijom određeno je u skladu s graničnim vrijednostima NDVI pa su tako izrađeni grafikoni godišnjeg maksimalnog NDVI i područja pod vegetacijom. Prilagodili smo liniju trenda varijacije maksimalne vrijednosti NDVI i područja pod vegetacijom kako bi se smanjio učinak meteoroloških čimbenika na NDVI vrijednosti. Rezultati pokazuju da se poslije zatvaranja jame i čišćenja područja odlaganja, naglo, tijekom zadnjeg desetljeća, povećao NDVI rudnika s otvorenim jamskim otvorom i područja pod vegetacijom, a ekološko okruženje tih rudnika se očito poboljšalo. Rudarske aktivnosti su dovele do naglog opadanja godišnjeg maksimalnog NDVI i područja pod vegetacijom s trajno smrznutim slojem tla, a ekološko okruženje rudnika se nastavlja pogoršavati. Premda četverogodišnji prosječni NDVI ostaje nepromijenjen u dijelovima nalazišta ugljena koji se eksploatiraju, a nemaju trajno smrznuti sloj tla, područje ugljenokopa pod vegetacijom se linearno smanjuje, ukazujući na činjenicu da se ekološko okruženje ugljenokopa pogoršava. Sa stajališta zaštite ekološkog okruženja, rezultati ovog istraživanja čine osnovu za donošenje odluke o otvaranju velikih rudnika s otvorenim jamskim oknom u hladnim i sušnim područjima.Evaluating the deterioration of ecological environment and vegetation of coalfields caused by China’s large-scale coal mining activities is important because of the fragile ecological environment and low temperature in cold and arid areas. This study takes the open coal pits of Haizhou, Gulianhe, and Huolinhe as examples and proposes a method for evaluating their ecological environment using Landsat time series images based on the Normalized Difference Vegetation Index (NDVI) variations of open-pit coalfields in cold and arid zones. The average NDVI value of the mining area each month was calculated using Landsat image data from 2000 to 2015. The vegetation cover area in the coalfields was extracted according to the NDVI threshold, and the scatter plots of the annual maximum NDVI and vegetation cover area were drawn. We fitted the variation trend line of maximum NDVI value and vegetation cover area to reduce the effect of meteorological factors on NDVI values. Results show that after the closure of open pit and reclamation of dump area, the NDVI of open-pit coalfields and vegetation cover area have been increasing rapidly over the last decade, and the ecological environment of these coalfields has obviously improved. The coal mining activities have led to the rapid decline of annual maximum NDVI and vegetation cover area of the coalfields in permafrost zones, and the ecological environment of coalfields continues to deteriorate. Although the quarterly average NDVI remains unchanged in non-permafrost mining coalfields under coal exploitation, the vegetation cover area in the coalfields decreases linearly, indicating that the ecological environment of the coalfields tends to deteriorate. From an ecological environment protection perspective, the results of this study provide a basis for decision making in constructing large-scale open pits in cold and arid zones

Molecular mechanism of point mutation-induced Monopolar spindle 1 (Mps1/TTK) inhibitor resistance revealed by a comprehensive molecular modeling study

Background Monopolar spindle 1 (Mps1/TTK) is an apical dual-specificity protein kinase in the spindle assembly checkpoint (SAC) that guarantees accurate segregation of chromosomes during mitosis. High levels of Mps1 are found in various types of human malignancies, such as glioblastoma, osteosarcoma, hepatocellular carcinoma, and breast cancer. Several potent inhibitors of Mps1 exist, and exhibit promising activity in many cell cultures and xenograft models. However, resistance due to point mutations in the kinase domain of Mps1 limits the therapeutic effects of these inhibitors. Understanding the detailed resistance mechanism induced by Mps1 point mutations is therefore vital for the development of novel inhibitors against malignancies. Methods In this study, conventional molecular dynamics (MD) simulation and Gaussian accelerated MD (GaMD) simulation were performed to elucidate the resistance mechanisms of Cpd-5, a potent Mps1 inhibitor, induced by the four representative mutations I531M, I598F, C604Y, S611R. Results Our results from conventional MD simulation combined with structural analysis and free energy calculation indicated that the four mutations weaken the binding affinity of Cpd-5 and the major variations in structural were the conformational changes of the P-loop, A-loop and αC-helix. Energetic differences of per-residue between the WT system and the mutant systems indicated the mutations may allosterically regulate the conformational ensemble and the major variations were residues of Ile-663 and Gln-683, which located in the key loops of catalytic loop and A-loop, respectively. The large conformational and energetic differences were further supported by the GaMD simulations. Overall, these obtained molecular mechanisms will aid rational design of novel Mps1 inhibitors to combat inhibitor resistance

Beneficial effects of baicalein on a model of allergic rhinitis

Allergic rhinitis (AR) is a common disease that causes severe inflammation and even disabilities. Previous studies have reported baicalein to have an anti-inflammatory effect. However, the pharmacological action of baicalein on anaphylaxis has not been clarified yet. This study assessed the in vivo protective effect of baicalein post-treatment in an ameliorating ovalbumin (OVA)-sensitized AR rat model. Baicalein attenuated histological alterations, aberrant tissue repair and inflammation after OVA-induced AR. Baicalein reduced the frequency of nasal/ear rubs and sneezes in rats, and inhibited generation of several inflammatory cytokines (TNF-α, IL-1β, and IL-6) in both blood and nasal lavage of rats. Infiltrations of eosinophils, lymphocyte, and neutrophils were decreased in baicalein-administered rats. Furthermore, baicalein inhibited the expression of STAT3 phosphorylation in the nasal mucosa. In summary, baicalein attenuated OVA-induced AR and inflammation, which suggests it as a promising therapeutic agent for the alleviation of AR-associated inflammation and pathology