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Symmetry analysis of Optical Coherence Tomography (OCT) images in right and left eyes can lead to introduction of new biomarkers for early detection of eye diseases. In this study, we investigate the symmetry between two eyes by calculating local cup to disc ratio (CDR) from each B-scan based on fusion of fundus images and OCT B-scans. For this purpose, in the first phase, the OCT data of Optic Nerve Head (ONH) in right and left eyes are aligned by finding the equivalent B-scans of both eyes based on the fovea-ONH axes. Since the fovea-ONH axes in OCT data are not available, at first, left and right fundus images are aligned according to their automatically detected fovea-ONH axes. Then, OCT data are registered to corresponding fundus images based on maximum similarity between en-face OCT data and fundus image in each eye. This two-stage alignment procedure depends on 1) the blood vessels, automatically extracted by Hessian analysis of directional curvelet sub-bands, and 2) the disc contour of ONH in fundus images, detected by Distance Regularized Level Set Evolution (DRLSE) algorithm. In the second phase, in order to calculate the local CDRs, the disc and cup boundaries are extracted from the aligned B-scans in left and right eyes. The disc boundary is limited by Bruch-Membrane opening, and the cup boundary is defined by retinal layer border. Therefore, Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) layers are extracted using ridgelet transform to find the disc-edge point and cup-edge point in each B-scan. Finally, the ratio of summed areas of cups to summed areas of discs is calculated in corresponding local regions to find the local CDRs. Using this method, we can also introduce a new index called local volumetric CDR (VCDR) by dividing the volume of cup in a specific region to the corresponding volume of disc extracted from OCT images of ONH. Forty healthy OCT datasets of size 650 × 512 × 128 (acquired from Topcon 3D OCT-1000) and corresponding 1536 × 1612 fundus images were used in this study. In addition to point-by-point comparison of CDRs in equivalent B-scans of aligned OCTs, the CDRs in upper, middle and lower regions were calculated and the maximum symmetry is observed in middle region. In addition, using local VCDR, the symmetry of 3D OCTs of both eyes is analyzed in 24 volumetric sectors.

This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a low-rank regularization approach is proposed for exploiting nonlocal self-similarity prior to OCT image reconstruction. To benefit from the advantages of the redundancy of multi-slice OCT data, we construct a third-order tensor by extracting the nonlocal similar three-dimensional blocks and grouping them by applying the k-nearest-neighbor method. Next, the nuclear norm is used as a regularization term to shrink the singular values of the constructed tensor in the non-local correlation direction. Further, the regularization approaches of the first-order tensor-based total variation (FOTTV) and SOTTV are proposed for better preservation of retinal layers and suppression of artifacts in OCT images. The alternative direction method of multipliers (ADMM) technique is then used to solve the resulting optimization problem. Our experiments show that integrating SOTTV instead of FOTTV into a low-rank approximation model can achieve noticeably improved results. Our experimental results on the denoising and super-resolution of OCT images demonstrate that the proposed model can provide images whose numerical and visual qualities are higher than those obtained by using state-of-the-art methods.

Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals of eye researchers. Material and Methods: The present study is designed in order to present a comparative study on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (MECNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). Results: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. Conclusion: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. This allows having a fast and robust computer aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.

Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model's performance, support vector machine and Naive Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models.

مقدمه: بررسی تقارن در عضوهایی از بدن که به صورت زوج وجود دارد، می‌تواند در تشخیص زودهنگام بیماری‌ها به پزشکان کمک کند. در این مطالعه، حد آستانه‌ی تقارن با محاسبه‌ی نسبت کاپ به دیسک در چشم افراد سالم با استفاده از داده‌های Optical coherence tomography (OCT) به صورت محلی و نقطه به نقطه با استفاده از هر B-scan بررسی شد. روش‌ها: در این مطالعه، 40 داده‌ی مربوط به افراد سالم که از دستگاه 3D-OCT1000 مدل تاپکن استخراج شده بود، مورد استفاده قرار گرفت. مراحل انجام کار به این صورت بود که با استخراج لایه‌ی Internal limiting membrane (ILM) و Retinal pigment epithelium (RPE)، محدوده‌ی کاپ و دیسک به ازای هر B-scan محاسبه شد و سپس به ازای B-scanهای معادل در چشم‌های چپ و راست، این نسبت به صورت نقطه به نقطه، مقایسه و پارامترهای تقارن بررسی قرار گردید. یافته‌ها: نسبت کاپ به دیسک در سه ناحیه‌ی فوقانی، میانی و تحتانی برای چشم‌های چپ و راست به دست آمد که به ترتیب برای چشم راست 054/0 ± 433/0، 061/0 ± 432/0 و 063/0 ± 405/0 و برای چشم چپ، 063/0 ± 441/0، 073/0 ± 443/0 و 067/0 ± بود؛ میزان اختلاف این نسبت برای دو چشم به طور میانگین 042/0 ± 048/0، 038/0 ± 042/0 و 045/0 ± 067/0 بود. نتیجه‌گیری: عدم تقارن در قسمت تحتانی دیسک نسبت به قسمت فوقانی و میانی بیشتر است و در قسمت میانی، بیشترین تقارن دیده می‌شود. همچنین در بررسی نقطه به نقطه، بیشترین تقارن در دومین B-scan در قسمت تحتانی محور افقی بین مرکز دیسک نوری و ماکولا مشاهده می‌شود.

Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.

In this experiment, a gene selection technique was proposed to select a robust gene signature from microarray data for prediction of breast cancer recurrence. In this regard, a hybrid scoring criterion was designed as linear combinations of the scores that were determined in the mutual information (MI) domain and protein-protein interactions network. Whereas, the MI-based score represents the complementary information between the selected genes for outcome prediction; and the number of connections in the PPI network between the selected genes builds the PPI-based score. All genes were scored by using the proposed function in a hybrid forward-backward gene-set selection process to select the optimum biomarker-set from the gene expression microarray data. The accuracy and stability of the finally selected biomarkers were evaluated by using five-fold cross-validation (CV) to classify available data on breast cancer patients into two cohorts of poor and good prognosis. The results showed an appealing improvement in the cross-dataset accuracy in comparison with similar studies whenever we applied a primary signature, which was selected from one dataset, to predict survival in other independent datasets. Moreover, the proposed method demonstrated 58-92 percent overlap between 50-genes signatures, which were selected from seven independent datasets individually.

This paper presents a new fully automatic algorithm for classification of 3D Optical Coherence Tomography (OCT) images as Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), and healthy people. The proposed algorithm does not need to any retinal layer alignment and also segmentation processes (e.g., segmentation of intra-retinal layers and lesion structures). The algorithm utilizes a new Wavelet-based Convolutional Mixture of Experts (WCME) model as an adaptive feature extraction and classification method. The WCME benefits from spatial-frequency decomposition and also an ensemble of convolutional neural networks (CNNs) to build a high-level representation of OCT data. In this study, a retinal OCT data set constituted of 148 cases is used for evaluation of the method based on unbiased cross-validation approach. The dataset consists of 50 normal, 50 DME, and 48 AMD OCT acquisitions from Heidelberg device. With the proposed WCME model, the overall algorithm accurately classified the OCT data with a precision rate of 95.21% and an area under the ROC curve (AUC) of 0.986.

Automatic segmentation of retinal boundaries in Optical Coherence Tomography (OCT) acquired around Optic Nerve Head (ONH) is of high importance, due to anatomical structure of this area. Exclusion of this area from calculations is a prevalent method but for cases like calculation of disc margins, it is not applicable. The pro-posed method is designed for exact localization of Retinal pigment epithelium break points in ONH centered OCTs. The method is based on incorporation of ridgelet transform for determination of the ONH region and consequent identification of the end points. Incorrect localization in presence of shadow of retinal vessels has also been considered to obtain the high accuracy. Considering the mean value of disk diameter (145.07±38.96), the rational error of the algorithm is 0.07.

Background: Various techniques are used in diagnosing cardiac diseases, one of those is using electrocardiogram tool. In special situations, like ambulatory care and emergency condition, using vectorcardiogram (VCG) regarding its fewer electrodes is much easier and more useful. However, most cardiologists are accustomed to the 12-lead electrocardiogram, even though some of the leads are either nearly aligned with or derived from the others and consequently contain redundant information. The ability to transform from orthogonal 3-lead vectorcardiogram to 12-lead electrocardiogram enables the use of fewer leads for computer visualization, signal analysis and wireless transmission of signals. This can also improve mobility, albeit limited, to the patients. Methods: Vectorcardiogram and electrocardiogram signals used in this study obtained from 50 healthy persons. We presented a statistical approach to transform 3-lead Frank vectorcardiogram to 12-lead electrocardiogram signals and vice versa, based on partial linear transformation (Least Square Method). In addition, our linear transformation function would be compared with Dower and affine transformation function. Findings: For healthy subjects, the partial linear transformation method had different transformation coefficients from other existed methods like Dower and affine ones. Using these new coefficients in partial linear method lead to gain more accurate statistical parameters in calculating the efficiency and accuracy of new method. Conclusion: Regarding the obtained results in this study, electrocardiogram signals derived from vectorcardiogram signals by using our method had more correlation with general electrocardiogram leads than ones derived by using Dower transformation. Therefore, by using this transformation function, achieving to general information of the case’s heart would be possible and useful.

CXCR4 is involved in inflammation, cancer metastasis and also HIV-1 entry into immune host cells. In the present research, it was decided to investigate the efficacy of some CXCR4 inhibitors from both pharmacokinetics and pharmacodynamics points of view. Quantitative structure–property relationship (QSPR) approach was applied to model the metabolic stability and instability of the compounds. Using QSPR modeling, it was tried to predict the metabolic stability using new hybrid algorithm which consisted of three different steps: descriptor reduction (PCA), stable–instable classification (KNN) and biological stability prediction (PLS). In the QSPR step, it is shown that the descriptor reduction (PCA) affects the result of the classification procedure (KNN). Besides, the obtained QSPR model can predict the metabolic stability of the stable compounds with [Formula: see text] of 0.98 for train data and of 0.64 for test data. In other words, increment and decrement of stability were followed by the model. Molecular docking simulation was exploited to define the essential interactions of an effective inhibitor with CXCR4 receptor.

In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task.

Symmetry analysis of right and left eyes can be a useful tool for early detection of eye diseases. In this study, we want to compare the Optical Coherent Tomography (OCT) images captured from optic nerve head (ONH) of right and left eyes. To do this, it is necessary to align the OCT data and compare equivalent B-scans in right and left eyes. For this reason, since the fovea-ONH axes in OCT data are not available due to small field of view in OCT, at first the projection of OCT data of each eye is registered to its corresponding fundus image using extracted vessels by Hessian analysis of directional curvelet subbands. Then, by alignment of fundus images of right and left eyes according to their automatically detected fovea-ONH axes, OCT projections are also aligned. After alignment of OCT projections, aligned B-scans are estimated and used for comparing different parameters such as cup-to-disk ratio (CDR). Using aligned B-scans, two signals of CDRs are obtained from two eyes which each point in these signals corresponds to CDR in a specific part of ONH, i.e., a point-to-point comparison between CDRs of right and left eyes is provided which has potential to lead to a new imaging biomarker for eye disease detection.

The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

Background: Admission includes written and interview at universities belonging to the ministry of the health and medical education of Iran at PhD level. In the present work, it was tried to find out the likelihood of interview performance of different candidates with their teaching experience in Iranian national medical PhD admission in the year 1386-87. Methods and Materials: In this study, applicants' exam results were extracted from their score workbooks for year 86-87. PhD applicants' categories were public (ordinary) and employed lecturers. Invited numbers of candidates for interview were 556 from 29 different fields of study. As the number of written subjects were not the same within different fields of study, at the first, each group score distribution were normalized to one and then combined together for final consideration. Results: Accept and reject percentage within public applicants were 45.1 and 54.9, respectively, while the accept percentage within lecturer applicants was 66 and the reject was 34 respectively. Scores of all 29 groups were combined after normalization. The overall performance including test plus interview for public and lecturers were 1.02 ± 0.12 and 0.95 ± 0.1, respectively. The average and standard deviation of test exam of public and lecturer were 1.04 ± 0.16 and 0.91 ± 0.12, respectively. The average and standard deviation of interview exam of public applicants and lecturers applicants were 0.98 ± 0.18 and 1.04 ± 0.17, respectively. Conclusion: As results show, the interview performance of lecturers is better than public applicants. Unbalanced acceptance rate amongst lecturers was increased due to the hold of reservation toward interview and due to their higher results gain during interview. If the test performance was a reliable measure for viability of applicant, this reservation would change the acceptance rate close to balance.

Various techniques are used in diagnosing cardiac diseases. One of these techniques is using electrocardiogram (ECG) tool. In special cardiac cases like atrial fibrillation and posterior myocardial infraction the cardiologist need some information from posterior side of the patient heart, that it can be achieved by using right-posterior ECG method (17 lead ECG). In right-posterior method, position of the patient must be changed in his/her side, so time is waste and patient would be more tired because of taking ECG signals two times. In this study vectorcardiogram (VCG) signals are used as a tool for providing posterior information of the heart. However because for cardiologists is much easier to work with ECG signals for detecting some cardiac diseases, in this study a new method using partial linear transformation is introduced to get posterior ECG leads (V7, V8, V9) from VCG signal. VCG and ECG signals that were used in this study obtained from 30 healthy persons. We presented a statistical approach to transform 3-lead Frank VCG to 15-lead ECG signals and vice versa, based on partial linear transformation (Least Square Method). Also our linear transformation function would be compared with affine transformation functions. The recorder device was Cardiax digital recorder system. The results show that for healthy subjects, the partial linear transformation (least square method) that is presented in this paper maps 3-lead VCG to15-lead ECG, is more accurate than affine transformation function. Regarding the obtained results in this study, ECG signals that derived from VCG signals by using our method was more similar to measured ECG signals than ones derived by using affine transformation. Therefore, by using this transformation function achieving to posterior information of the case heart would be more accurate and useful.

This article is about applying a new pre-compensation method on digital images displayed on computer monitors. The proposed method can be categorized as a deconvolution technique, based on human ocular point spread function (PSF). It's possible to obtain the ocular PSF from “wavefront analyzers”, an accessible optometric apparatus for measuring the optical aberrations of human eye. Optical aberration is any deviation of the optical wavefront from its ideal path. This pre-compensation algorithm that is based on an enhanced local Wiener filter and edge tapering was tested on 8 different types of aberrations. For this purpose, we used the Zernike polynomials to represent the mentioned 8 common varieties of ocular optical aberrations.

Optical coherence tomography (OCT) is an effective and noninvasive modality for retinal imaging. 3-D data that acquired from 3-D Spectral Domain OCT (SD-OCT) have shown their importance in the evaluation of retinal diseases. In addition, this set of data provides an opportunity to study depth of retina. In this paper, we focus on forming X-Y axis images from each layer of retina. In this manner, we first choose diffusion map based segmentation for localization of 12 different boundaries in 3D retinal data. Then we take an average on layers which located between each pairs of detected boundaries. Therefore, we make the X-Y axis image from each layer. With wavelet based image fusion, we combine together the layers with appropriate information to make images with additional information in retinal depth.

In this paper a method for eliminating the unwanted black parts on the margins of the copied image from a book is proposed, reduction of which makes the image look better as well as reducing the consumption of toner by the photocopy machine significantly. This method is able to reduce % 89.58 of the unwanted black parts (in average) compared to a manual method. Furthermore it makes %44.79 reduction in consumption of the toner by a photocopy machine.

Since the QRS complex in electrocardiogram signals is one of the most important tasks to describe the operation of heart, high accuracy detection for this complex should be considered. In this study one of the newest methods of QRS complex detection combined with several artifact sources reduction methods has been performed. QRS detection algorithm includes baseline drift removal, Butterworth filtering, notch filtering and extracting five special features from ECG to identify QRS complex. In order to validate the robustness of this method, four important artifact sources such as power line interference, electrode contact noise, motion artifact and muscle contraction (EMG) have been produced and combined with ECG signal. The performance of this approach against these noises based on three MIT-BIH recording classes (Normal, LQT and TWA) has been discussed with ROC (Receiver Operating Characteristics). With proposed QRS detection algorithm 100% and 94.88% accuracy has been achieved in best and worst case respectively. Thus this method has the ability to cancel respiration modulation and reduce EMG noise, motion and power line artifacts effectively.

Anesthesia is a vital and important part of any surgical practice and allows doctors to operate safely and painlessly on a patients. The wide variety of available anesthetics allows anesthesiologists to select the most suitable type of anesthesia and anesthetic drug for a patient. Providing balanced anesthesia by testing the depth of anesthesia (DOA) is a way to know sudden awareness or increasing level of anesthesia during a surgery. In this paper, we present several methods to analyze the results of our experiments performed on 33 patients under coronary vessel surgery. In the first method, by applying the wavelet transform on EEG signal, a new index (namely WAI) is obtained, which shows the conscious level of the patient. In the second method, 10 features related to EEG signal during 10 second windows, such as, edge frequency, and beta ratio, are extracted and used by neural and neuro-fuzzy networks as inputs. Then, the value of DOA is calculated for each of the used algorithms. The correlation value of these methods, which is a criterion of the accuracy, is shown by the BIS monitor output. Simulation results show that the highest amount of correlation is achieved using neural networks with respect to BIS index.

As the T-wave section in electrocardiogram (ECG) illustrates the repolarization phase of heart activity, the information which is accumulated in this section is so significant that it can explain the proper operation of electrical activities in heart. Long QT syndrome (LQT) and T-Wave Alternans (TWA) have imperceptible effects on time and amplitude of T-wave interval. Therefore, T-wave shapes of these diseases are similar to normal beats. Consequently, several T-wave features can be used to classify LQT and TWA diseases from normal ECGs. Totally, 22 features including 17 morphological and 5 wavelet features have been extracted from T-wave to show the ability of this section to recognize the normal and abnormal records. This recognition can be implemented by pre-processing, T-wave feature extraction and artificial neural network (ANN) classifier using Multi Layer Perceptron (MLP). The ECG signals obtained from 142 patients (40 normal, 47 LQT and 55 TWA) are processed and classified from MIT-BIH database. The specificity factor for normal, LQT, and TWA classifications are 99.89%, 99.90%, and 99.43%, respectively. T-wave features are one of the most important descriptors for LQT syndrome, Normal and TWA of ECG classification. The morphological features of T-wave have also more effect on the classification performance in LQT, TWA and normal samples compared with the wavelet features.