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Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images. [ABSTRACT FROM AUTHOR]

It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable. [ABSTRACT FROM AUTHOR]

Background: In this paper, the method of designing a noninvasive device for eliminating hand tremors in Parkinson's patients is presented. The designed device measures the tremors of the patient's hand and implements the tremor control accordingly. Since Parkinson's disease reduces patients' abilities to perform daily activities, this device is designed as an electronic spoon. The inertial measurement units are used to measure hand tremors. Method: The signals got from motion sensors are passed through Butterworth's second order low pass filters to attenuate signals amplitude at frequencies higher than the human hand's natural frequency. The signals are sent to a proposed Proportional Integral (PI) fuzzy controller as a set point signal, and appropriate control signals are applied to two actuators installed orthogonal. Besides motion sensors, a microcontroller is installed inside the spoon handle that implements a PI fuzzy controller and provides control signals for two high speed servo motors installed perpendicularly. Results: As such, the spoon can minimize the tremor effect. In this system, no damper or mass is added to the hand, and the patients are not required to wear an orthosis. The contribution of this paper is twofold. First, we use sensor data fusion to increase measurement accuracy. In this paper, we use accelerometer and gyroscope sensors. Second, we proposed a robust PI fuzzy controller to compensate for the uncertainties and reduce the tremor. Conclusion: The test results show that the hand tremor of Parkinson's patients during eating is reduced up to 75% using this method. [ABSTRACT FROM AUTHOR]

Background: The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer's aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA). Methods: Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label. Results: Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98. Conclusion: Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results. [ABSTRACT FROM AUTHOR]

Most of the countries with elderly populations are currently facing with chronic diseases. In this regard, Internet of Things (IoT) technology offers promising tools for reducing the chronic disease burdens. Despite the presence of fruitful works on the use of IoT for chronic disease management in literature, these are rarely overviewed consistently. The present study provides an overview on the use of IoT for chronic disease management, followed by ranking different chronic diseases based on their priority for using IoT in the developing countries. For this purpose, a structural coding was used to provide a list of technologies adopted so far, and then latent Dirichlet allocation algorithm was applied to find major topics in literature. In order to rank chronic diseases based on their priority for using IoT, a list of common categories of chronic diseases was subjected to fuzzy analytic hierarchy process. The research findings include lists of IoT technologies for chronic disease management and the most-discussed chronic diseases. In addition, with the help of text mining, a total of 18 major topics were extracted from the relevant pieces of literature. The results indicated that the cardiovascular disease and to a slightly lesser extent, diabetes mellitus are of the highest priorities for using IoT in the context of developing countries. [ABSTRACT FROM AUTHOR]

Ventricular arrhythmias are one of the most important causes of annual deaths in the world, which may lead to sudden cardiac deaths. Accurate and early diagnosis of ventricular arrhythmias in heart diseases is essential for preventing mortality in cardiac patients. Ventricular activity on the electrocardiogram (ECG) signal is in the interval from the beginning of QRS complex to T wave end. Variations in the ECG signal and its features may indicate heart condition of patients. The first step to extract features of ECG in time domain is finding R peaks. In this paper, a combination of two algorithms of Pan-Tompkins and state logic machine has been used to find R peaks in heart signals for normal sinus signals and ventricular abnormalities. Then, a healthy or sick beat may be realized by comparing the difference between R peaks obtained from two algorithms in each beat. The morphological features of the ECG signal in the range of QRS complex are evaluated. Ventricular tachycardia (VT), ventricular flutter (VFL), ventricular fibrillation (VFI), ventricular escape beat (VEB), and premature ventricular contractions (PVCs) are abnormalities studied in this paper. In the classification step, the support vector machine (SVM) classifier with Gaussian kernel (one in front of everyone) is used. Accuracy percentages of ventricular abnormalities mentioned above and normal sinus rhythm are respectively obtained as 95.8%, 92.8%, 94.5, 98.9%, 91.5%, and 100%. The database of this paper has been taken from normal sinus rhythm and MIT-SCD banks available on Physionet.org. [ABSTRACT FROM AUTHOR]

Backgorund: Nowadays, everybody's life is dominated by COVID-19, which might have been the source of severe acute respiratory syndrome coronavirus 2. This virus disrupts the lungs first of all. Recently, it has been found that coronavirus may affect the brain. Because all body actions rely on the brain, hence investigating its healthy is an essential item in coronavirus effects. Method: Brain image segmentation can be helpful in the detection of the regions damaged by the effects of coronavirus. Since every image given by photography devices may have noises, therefore, first of all, the brain magnetic resonance angiography (MRA) images must be denoised for best investigation. In the present paper, we have presented the construction of multishearlets based on multiwavelets for the first time and have used them for the purpose of denoising. Multiwavelets have some advantages to wavelets. Therefore, we have used them in the shearlet system to expand the properties of multiwavelets in all directions. After denoising, we have proposed a scheme for the automatic characterization of the initial curve in the active contour model for segmentation. Detecting the initial curve is a challenging task in active contour-based segmentation because detecting an initial curve far from the desired region can lead to unfavorable results. Results: The results show the performance of using multishearlets in detecting affected regions by COVID-19. Using multishearlets has led to the high value of peak signal-to-noise ratio and Structural similarity index measure in comparison with original shearlets. Original shearlets are constructed from wavelets whereas we have constructed multishearlets from multiwavelets. Conclusion: The results show that multishearlets can neutralize the effect of noise in MRA images in a good way rather than shearlets. Moreover, the proposed scheme for segmentation can lead to 0.99 accuracy. [ABSTRACT FROM AUTHOR]

Background: The obstructive sleep apnea (OSA) detection has become a hot research topic because of the high risk of this disease. In this paper, we tested some powerful and low computational signal processing techniques for this task and compared their results with the recent achievements in OSA detection. Methods: The Dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. From these coefficients, eight non-linear features are extracted and then reduced by the Multi-cluster feature selection (MCFS) algorithm. The remaining features are applied to the hybrid "K-means, RLS" RBF network which is a low computational rival for the Support vector machine (SVM) networks family. Results and Conclusion: The results showed suitable OSA detection percentage near 96% with a reduced complexity of nearly one third of the previously presented SVM based methods. [ABSTRACT FROM AUTHOR]

Background: The fusion of images is an interesting way to display the information of some different images in one image together. In this paper, we present a deep learning network approach for fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Methods: We fused two MRI and PET images automatically with a pretrained convolutional neural network (CNN, VGG19). First, the PET image was converted from red-green-blue space to hue-saturation-intensity space to save the hue and saturation information. We started with extracting features from images by using a pretrained CNN. Then, we used the weights extracted from two MRI and PET images to construct a fused image. Fused image was constructed with multiplied weights to images. For solving the problem of reduced contrast, we added the constant coefficient of the original image to the final result. Finally, quantitative criteria (entropy, mutual information, discrepancy, and overall performance [OP]) were applied to evaluate the results of fusion. We compared the results of our method with the most widely used methods in the spatial and transform domain. Results: The quantitative measurement values we used were entropy, mutual information, discrepancy, and OP that were 3.0319, 2.3993, 3.8187, and 0.9899, respectively. The final results showed that our method based on quantitative assessments was the best and easiest way to fused images, especially in the spatial domain. Conclusion: It concluded that our method used for MRI-PET image fusion was more accurate. [ABSTRACT FROM AUTHOR]

Background: The main goal of ultrasound therapy is to have clinical effects in the tissue without damage to the intervening and surrounding tissues. Treatments have been developed for both in vitro and in clinical applications. HIFU therapy is one of these. Non-invasive surgeries, such as HIFU, have been developed to treat tumors or to stop bleeding. In this approach, an adequate imaging method for monitoring and controlling the treatment is required. Methods: In this paper, an adaptive compressive sensing representation of ultrasound RF echo signals is presented based on empirical mode decomposition (EMD). According to the different numbers of intrinsic mode functions (IMFs) produced by the EMD, the ultrasound signals is adaptively compressive sampled in the source and then adaptively reconstructed in the receiver domains. In this paper, a new application of compressive sensing based on EMD (CS-EMD) in the monitoring of high-intensity focused ultrasound (HIFU) treatment is presented. Non-invasive surgeries such as HIFU have been developed for various therapeutic applications. In this technique, a suitable imaging method is necessary for monitoring of the treatment to achieve adequate treatment safety and efficacy. So far, several methods have been proposed, such as ultrasound radiofrequency (RF) signal processing techniques, and imaging methods such as X-ray, MRI, and ultrasound to monitor HIFU lesions. Results: In this paper, a CS-EMD method is used to detect the HIFU thermal lesion dimensions using different types of wavelet transform. The results of the processing on the real data demonstrate the potential for this technique in image-guided HIFU therapy. Conclusions: In this study, a new application of compressive sensing in the field of monitoring of the HIFU treatment is presented. To the best of our knowledge, so far no studies on compressive sensing have been carried out in the monitoring of the HIFU. Based on the results obtained, it was showed that the number of measurements and Intrinsic Mode Functions have the function of noise reduction. In addition, results were shown that the successful reconstruction of the compressive sensing signals can be gained using a threshold based algorithm. To this end, in this paper it was shown that by selecting an suitable number of measurements, the sparse transform, and a thresholding algorithm, we can achieve a more accurate detection of the HIFU thermal lesion size. [ABSTRACT FROM AUTHOR]

Background: Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement. Method: In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm. Result: This enables the method to do the super-resolution and de-noising simultaneously. Conclusion: Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations. [ABSTRACT FROM AUTHOR]

Background: In Persian medicine (PM), measuring the wrist pulse is one of the main methods for determining a person's health status and temperament. One problem that can arise is the dependence of the diagnosis on the physician's interpretation of pulse wave features. Perhaps, this is one reason why this method has yet to be combined with modern medical methods. This paper addresses this concern and outlines a system for measuring pulse signals based on PM. Methods: A system that uses data from a customized device that logs the pulse wave on the wrist was designed and clinically implemented based on PM. Seven convolutional neural networks (CNNs) have been used for classification. Results: The pulse wave features of 34 participants were assessed by a specialist based on PM principles. Pulse taking was done on the wrist in the supine position (named Malmas in PM) under the supervision of the physician. Seven CNNs were implemented for each participant's pulse characteristic (pace, rate, vessel elasticity, strength, width, length, and height) assessment, and then, each participant was classified into three classes. Conclusion: It appears that the design and construction of a customized device combined with the deep learning algorithm can measure the pulse wave features according to PM and it can increase the reliability and repeatability of the diagnostic results based on PM. [ABSTRACT FROM AUTHOR]

Background: Recently, a marginalized particle-extended Kalman filter (MP-EKF) has been proposed for electrocardiogram (ECG) signal denoising. Similar to particle filters, the performance of MP-EKF relies heavily on the definition of proper particle weighting strategy. In this paper, we aim to investigate the performance of MP-EKF under different particle weighting strategies in both stationary and nonstationary noises. Some of these particle weighting strategies are introduced for the first time for ECG denoising. Methods: In this paper, the proposed particle weighting strategies use different mathematical functions to regulate the behaviors of particles based on noisy measurements and a synthetic ECG signal built using feature parameters of ECG dynamic model. One of these strategies is a fuzzy-based particle weighting method that is defined to adapt its function based on different input signal-to-noise ratios (SNRs). To evaluate the proposed particle weighting strategies, the denoising performance of MP-EKF was evaluated on MIT-BIH normal sinus rhythm database at 11 different input SNRs and in four different types of artificial and real noises. For quantitative comparison, the SNR improvement measure was used, and for qualitative comparison, the multi-scale entropy-based weighted distortion measure was used. Results: The experimental results revealed that the fuzzy-based particle weighting strategy exhibited a very well and reliable performance in both stationary and nonstationary noisy environments. Conclusion: We concluded that the fuzzy-based particle weighting strategy is the best-suited strategy for MP-EKF framework because it adaptively and automatically regulates the behaviors of particles in different noisy environments. [ABSTRACT FROM AUTHOR]

The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets. [ABSTRACT FROM AUTHOR]

Background: In this paper, a novel approach is proposed for the recognition of Persian phonemes in the Persian consonant-vowel combination (PCVC) speech dataset. Nowadays, deep neural networks (NNs) play a crucial role in classification tasks. However, the best results in speech recognition are not yet as perfect as human recognition rate. Deep learning techniques show outstanding performance over many other classification tasks, such as image classification and document classification. Furthermore, the performance is sometimes better than a human. The reason why automatic speech recognition systems are not as qualified as the human speech recognition system, mostly depends on features of data which are fed to deep NNs. Methods: In this research, first, the sound samples are cut for the exact extraction of phoneme sounds in 50 ms samples. Then, phonemes are divided into 30 groups, containing 23 consonants, 6 vowels, and a silence phoneme. Results: The short-time Fourier transform is conducted on them, and the results are given to PPNet (a new deep convolutional NN architecture) classifier and a total average of 75.87% accuracy is reached which is the best result ever compared to other algorithms on separated Persian phonemes (like in PCVC speech dataset). Conclusion: This method not only can be used for recognizing mono-phonemes but it can also be adopted as an input to the selection of the best words in speech transcription [ABSTRACT FROM AUTHOR]

Background: Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research; therefore, EEG signal-based methods are the most accurate and informative. Methods: In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject-dependent and subject-independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three-second epoch of received signals on the embedded system, which can be applied in real-time usage in practice. Results: Average classification accuracies of 99.10% in the valence and 99.20% in the arousal for subject-dependent and 90.76% in the valence and 90.94% in the arousal for subject independent were achieved on the well-known DEAP dataset. Conclusion: Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice. [ABSTRACT FROM AUTHOR]

When an epileptic seizure occurs, the neuron's activity of the brain is dynamically changed, which affects the connectivity between brain regions. The connectivity of each brain region can be quantified by electroencephalography (EEG) coherence, which measures the statistical correlation between electrodes spatially separated on the scalp. Previous studies conducted a coherence analysis of all EEG electrodes covering all parts of the brain. However, in an epileptic condition, seizures occur in a specific region of the brain then spreading to other areas. Therefore, this study applies an energy-based channel selection process to determine the coherence analysis in the most active brain regions during the seizure. This paper presents a quantitative analysis of inter- and intrahemispheric coherence in epileptic EEG signals and the correlation with the channel activity to glean insights about brain area connectivity changes during epileptic seizures. The EEG signals are obtained from ten patients' data from the CHB-MIT dataset. Pair-wise electrode spectral coherence is calculated in the full band and five sub-bands of EEG signals. The channel activity level is determined by calculating the energy of each channel in all patients. The EEG coherence observation in the preictal (Cohpre) and ictal (Cohictal) conditions showed a significant decrease of Cohictal in the most active channel, especially in the lower EEG sub-bands. This finding indicates that there is a strong correlation between the decrease of mean spectral coherence and channel activity. The decrease of coherence in epileptic conditions (Cohictal pre) indicates low neuronal connectivity. There are some exceptions in some channel pairs, but a constant pattern is found in the high activity channel. This shows a strong correlation between the decrease of coherence and the channel activity. The finding in this study demonstrates that the neuronal connectivity of epileptic EEG signals is suitable to be analyzed in the more active brain regions. [ABSTRACT FROM AUTHOR]

Temporal segmentation of laparoscopic video is the first step toward identifying anomalies and interrupts, recognizing actions, annotating video and assessing the surgeons' learning curve. In this paper, a novel approach for temporal segmentation of minimally-invasive videos (MIVS) is proposed. Illumination variation, shadowing, dynamic backgrounds and tissue respiratory motion make it challenging to extract information from laparoscopic videos. These challenges if not properly addressed could increase the errors of data extraction modules. Therefore, in MIVS, several data sets are extracted from laparoscopic videos using different methods to alleviate error effects of data extraction modules on MIVS performance. Each extracted data set is segmented temporally with Genetic Algorithm (GA) after outlier removal. Three different cost functions are examined as objective function of GA. The correlation coefficient is calculated between objective values of the solutions visited by GA and their corresponding performance measures. Performance measures include detection rate, recognition rate and accuracy. Cost functions having negative correlations with all mentioned performance measures are selected. Finally, a multi-objective GA is executed on the data sets to optimize the selected cost functions. MIVS is tested on laparoscopic videos of varicocele and ureteropelvic junction obstruction surgeries collected from hasheminejad kidney center. Experimental results demonstrate that MIVS outperforms the state-of-the-art methods in terms of accuracy, detection rate and recognition rate. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal-to-interference (SIR) index, without destroying the original signal. After quick elimination of all undesirable signals, two successful feature extractors - the log-band power algorithm and common spatial patterns (CSPs) - are used to extract features. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during the imagination of the tongue, feet, and left-right-hand movement. Finally, three well-known classifiers are evaluated, where the ridge regression classifier and CSPs as feature extractor have the highest accuracy classification rate about 83.06% with a standard deviation of 1.22%, counterposing the recent studies. [ABSTRACT FROM AUTHOR]

Barrett's mucosa is one of the most important diseases in upper gastrointestinal system that caused by gastro-esophagus reflux. If left untreated, the disease will cause distal esophagus and gastric cardia adenocarcinoma. The malignancy risk is very high in short segment Barrett's mucosa. Therefore, lesion area segmentation can improve specialist decision for treatment. In this paper, we proposed a combined fuzzy method with active models for Barrett's mucosa segmentation. In this study, we applied three methods for special area segmentation and determination. For whole disease area segmentation, we applied the hybrid fuzzy based level set method (LSM). Morphological algorithms were used for gastroesophageal junction determination, and we discriminated Barrett's mucosa from break by applying Chan-Vase method. Fuzzy c-mean and LSMs fail to segment this type of medical image due to weak boundaries. In contrast, the full automatic hybrid method with correlation approach that has used in this paper segmented the metaplasia area in the endoscopy image with desirable accuracy. The presented approach omits the manually desired cluster selection step that needed the operator manipulation. Obtained results convinced us that this approach is suitable for esophagus metaplasia segmentation. [ABSTRACT FROM AUTHOR]

Nowadays, magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee's cyst (especially Baker) segmentation is one of the novel research areas. There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of Baker's cyst in the knee's joint MRI sagittal and axial T2-weighted images. The performance of this algorithm was investigated, and the results showed that in a short time Baker's cyst can be clearly extracted from original images in axial and sagittal planes. The marker-controlled watershed segmentation was able to detect Baker's cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MRI pathologies. [ABSTRACT FROM AUTHOR]

Background: P300 signal detection is an essential problem in many fields of Brain-Computer Interface (BCI) systems. Although deep neural networks have almost ubiquitously used in P300 detection, in such networks, increasing the number of dimensions leads to growth ratio of saddle points to local minimums. This phenomenon results in slow convergence in deep neural network. Hyperparameter tuning is one of the approaches in deep learning, which leads to fast convergence because of its ability to find better local minimums. In this paper, a new adaptive hyperparameter tuning method is proposed to improve training of Convolutional Neural Networks (CNNs). Methods: The aim of this paper is to introduce a novel method to improve the performance of deep neural networks in P300 signal detection. To reach this purpose, the proposed method transferred the non-convex error function of CNN) into Lagranging paradigm, then, Newton and dual active set techniques are utilized for hyperparameter tuning in order to minimize error of objective function in high dimensional space of CNN. Results: The proposed method was implemented on MATLAB 2017 package and its performance was evaluated on dataset of Ecole Polytechnique Fédérale de Lausanne (EPFL) BCI group. The obtained results depicted that the proposed method detected the P300 signals with 95.34% classification accuracy in parallel with high True Positive Rate (i.e., 92.9%) and low False Positive Rate (i.e., 0.77%). Conclusions: To estimate the performance of the proposed method, the achieved results were compared with the results of Naive Hyperparameter (NHP) tuning method. The comparisons depicted the superiority of the proposed method against its alternative, in such way that the best accuracy by using the proposed method was 6.44%, better than the accuracy of the alternative method. [ABSTRACT FROM AUTHOR]

It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1-enhanced magnetic resonance images of low-grade and high-grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0.42 of maximum value of difference of brightness between pixels that caused the 83.62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro-fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges. [ABSTRACT FROM AUTHOR]

The latest World Health Organization statistics show that the number of people living with COVID-19 disease is now more than 42 million worldwide. Some diagnosis methods include detecting and observing clinical symptoms associated with the disease (fever, dry cough, shortness of breath, sore throat, and muscle fatigue). Some other methods, such as computed tomography (CT)-scan imaging from the lungs, are the more accurate diagnostic methods. In this study, we examine the types of abnormal COVID-19 can cause in the lungs of infected subjects and detect and classify this disease. In this paper, we used data from the lung's CT-scan images from the 79 participants. To do this, in this article, for processing CT-scan images of the lungs to diagnose and classification of the COVID-19 disease in men and women of different ages, for rapid diagnosis and high accuracy of this disease by the automatic classification algorithm is used. The final results showed that the proposed method could base on different categories (gender, age categories, and type of damage caused by COVID-19) with high detection and classification accuracy. The algorithm presented in this article has accurately identified the data of healthy subjects and patients with coronavirus. [ABSTRACT FROM AUTHOR]

Recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. It is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. In this paper, for fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of cancerous zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of breast cancer regions by applying an appropriate threshold. For detection of cancerous zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and utilized in radial basis function neural network. Our simulation results indicate the accuracy of 92% classification using F1W2 method. [ABSTRACT FROM AUTHOR]

Background: Bacterial sensors are recommended for medical sciences, pharmaceutical industries, food industries, and environmental monitoring due to low cost, high sensitivity, and appropriate response time. There are some advantages of using bacterial spores instead of bacteria in vegetative forms as spores remain alive without any nutrient for a long time and change to vegetative form when a suitable environment is provided for them. Methods: For biosensor fabrication, it is important to define how the bacterial spores are delivered to the substrate media. The main purpose of this paper is an investigation of transferring bacterial spores on a flexible substrate media using a commercial inkjet printer (HP Deskjet 1510). It should be noted that in the previous researches, the special printers were used to transfer bacteria on rigid films. Results: These printed bacterial spores are used as a colorimetric temperature indicator. The custom-made bio-inks are prepared by bacterial spores along with a gelling agent and pH indicator. Conclusions: Finally, transformation of bacterial spores into vegetative bacteria is occurred by changing of temperature. A color change in the bio-prints is demonstrated because the bacterial transformation and growth change the environmental pH to an acidic level. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) is the first cause of world death, and myocardial infarction (MI) is one of the five primary disorders of CVDs which the patient electrocardiogram (ECG) analysis plays a dominant role in MI diagnosis. This research aims to evaluate some extracted features of ECG data to diagnose MI. Methods: In this paper, we used the Physikalisch-Technische Bundesanstalt database and extracted some morphological features, such as total integral of ECG, integral of the T-wave section, integral of the QRS complex, and J-point elevation from a cycle of normal and abnormal ECG waveforms. Since the morphology of healthy and abnormal ECG signals is different, we applied integral to different ECG cycles and intervals. We executed 100 of iterations on a 10-fold and 5-fold cross-validation method and calculated the average of statistical parameters to show the performance and stability of four classifiers, namely logistic regression (LR), simple decision tree, weighted K-nearest neighbor, and linear support vector machine. Furthermore, different combinations of proposed features were employed as a feature selection procedure based on classifier's performance using the aforementioned trained classifiers. Results: The results of our proposed method to diagnose MI utilizing all the proposed features with an LR classifier include 90.37%, 94.87%, and 86.44% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of 0.006. Conclusion: Our proposed classification-based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works. [ABSTRACT FROM AUTHOR]

Automating the camera Lucida method which is a standard way for focusing microscopic images is a very challenging study for many scientists. Hence, actually combining hardware and software to automate microscopic imaging systems is one of the most important issues in the field of medicine as well. This idea reduces scanning time and increases the accuracy of user's results in this field. Closed-loop control system has been designed and implemented in the hardware part to move the stage in predefined limits of 15°. This system produces 50 consecutive images from parasites at the mentioned spatial distances in two directions of the z-axis. Then, by introducing our proposed relational software with combining images, a high-contrast image can be presented. This colored image is focused on many subparts of the sample even with different ruggedness. After implementing the closed-loop controller, stages movement was repeated eight times with an average step displacement of 20 μm which were measured in two directions of the z-axis by a digital micrometer. On average, the movement's error was 1 μm. In software, the edge intensity energy index has been calculated for image quality evaluation. The standard camera Lucida method has been simulated with acceptable results based on experts' opinions and also mean squared error parameters. Mechanical movement in stage has an accuracy of about 95% which will meet the expectations of laboratory user. Although output-focused colored images from our combining software can be replaced by the traditional fully accepted Camera Lucida method. [ABSTRACT FROM AUTHOR]

Distribution of retinal blood vessels (RBVs) in retinal images has an important role in the prevention, diagnosis, monitoring and treatment of diseases, such as diabetes, high blood pressure, or heart disease. Therefore, detection of the exact location of RBVs is very important for Ophthalmologists. One of the frequently used techniques for extraction of these vessels is region growing-based Segmentation. In this paper, we propose a new region growing (RG) technique for RBVs extraction, called cellular automata-based segmentation. RG techniques often require manually seed point selection, that is, human intervention. However, due to the complex structure of vessels in retinal images, manual tracking of them is very difficult. Therefore, to make our proposed technique full automatic, we use an automatic seed point selection method. The proposed RG technique was tested on Digital Retinal Images for Vessel Extraction database for three different initial seed sets and evaluated against the manual segmentation of retinal images available at this database. Three quantitative criteria including accuracy, true positive rate and false positive rate, were considered to evaluate this method. The visual scrutiny of the segmentation results and the quantitative criteria show that, using cellular automata for extracting the blood vessels is promising. However, the important point at here is that the correct initial seeds have an effective role on the final results of segmentation. [ABSTRACT FROM AUTHOR]

Biologic scaffolds composed of extracellular matrix (ECM) are frequently used for clinical purposes of tissue regeneration. Different methods have been developed for this purpose. All methods of decellularization including chemical and physical approaches leave some damage on the ECM; however, the effects of these methods are different which make some of these procedures more proper to maintain ECM structure than other methods. This review is aimed to introduce and compare new physical methods for the decellularization of different tissues and organs in tissue engineering. All recent reports and research that have used at least one physical method in the procedure of decellularization, were included and evaluated in this paper. The advantages and drawbacks of each method were examined and compared considering the effectiveness. This review tried to highlight the prospective potentials and benefits of applying physical methods for decellularization protocols in tissue engineering instead of the current chemical methods. These chemical methods are harsh in nature and were shown to be destructive and harmful to essential substances of ECM and scaffold structure. Therefore, using physical methods as a partial or even a whole protocol could save time, costs, and quality of the final acellular tissue in complicated decellularization procedures. Moreover, regarding the control factor that could be achieved easily with physical methods, optimization of different decellularization protocols would be quite satisfactory. Combined methods take advantage of both chemical and physical approaches. [ABSTRACT FROM AUTHOR]

Background: In this paper, we have presented a new custom smart CMOS image sensor (CIS) for low power wireless capsule endoscopy. Method: The proposed new smart CIS includes a 256 × 256 current mode pixels array with a new on-chip adaptive neuro-fuzzy inference system that has been used to diagnosing bleeding images. We use a new pinned photodiode to realize the current mode of active pixels in the standard CMOS process. The proposed chip has been implemented in 0.18 μm CMOS 1P6M TSMC RF technology with a die area of 7 mm × 8 mm. Results and Conclusion: A built-in smart bleeding detection system on CIS leads to decrease in the RF transmitter power consumption near zero. The average power dissipation of the proposed smart CIS is 610 μW. [ABSTRACT FROM AUTHOR]

Rapid advances in biochemistry and genetics lead to expansion of the various medical instruments for detection and prevention tasks. On the other hand, food safety is an important concern which relates to the public health. One of the most reliable tools to detect bioparticles (i.e., DNA molecules and proteins) and determining the authenticity of food products is the optical ring resonators. By depositing a recipient polymeric layer of target particle on the periphery of an optical ring resonator, it is possible to identify the existence of molecules by calculating the shift in the spectral response of the ring resonators. The main purpose of this paper is to investigate the performance of two structures of optical ring resonators, (i) all-pass and (ii) add-drop resonators for sensing applications. We propose a new configuration for sensing applications by introducing a nanogap in the all-pass ring resonator. The performance of these resonators is studied from sensing point of view. Simulation results, using finite difference time domain paradigm, revealed that the existence of a nanogap in the ring configuration achieves higher amount of sensitivity; thus, this structure is more suitable for biosensing applications. [ABSTRACT FROM AUTHOR]

Wireless body area networks consist of several devices placed on the human body, sensing vital signs and providing remote recognition of health disorders. Low power consumption is crucial in these networks. A new energy-efficient topology is provided in this paper, considering relay and sensor nodes' energy consumption and network maintenance costs. In this topology design, relay nodes, placed on the cloth, are used to help the sensor nodes forwarding data to the sink. Relay nodes' situation is determined such that the relay nodes' energy consumption merges the uniform distribution. Simulation results show that the proposed method increases the lifetime of the network with nearly uniform distribution of the relay nodes' energy consumption. Furthermore, this technique simultaneously reduces network maintenance costs and continuous replacements of the designer clothing. The proposed method also determines the way by which the network traffic is split and multipath routed to the sink. [ABSTRACT FROM AUTHOR]

Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, kmeans clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell's image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method. [ABSTRACT FROM AUTHOR]

Background: Alcohol addiction contributes to disorders in brain's normal patterns. Analysis of electroencephalogram (EEG) signal helps to diagnose and classify alcoholic and normal EEG signal. Methods: One-second EEG signal was applied to classify alcoholic and normal EEG signal. To determine discriminative feature and EEG channel between the alcoholic and normal EEG signal, different frequency and non-frequency features of EEG signal, including power of EEG signal, permutation entropy (PE), approximate entropy (ApEn), katz fractal dimension (katz FD) and Petrosion fractal dimension (Petrosion FD) were extracted from alcoholic and normal EEG signal. Statistical analysis and Davis-Bouldin criterion (DB) were utilized to specify and select most discriminative feature and EEG channel between the alcoholic and normal EEG signal. Results: Results of statistical analysis and DB criterion showed that the Katz FD in FP2 channel showed the best discrimination between the alcoholic and normal EEG signal. The Katz FD in FP2 channel showed the accuracies of 98.77% and 98.5% by two classifiers with 10-fold cross validation. Conclusion: This method helps to diagnose alcoholic and normal EEG signal with the minimum number of feature and channel, which provides low computational complexity. This is helpful to faster and more accurate classification of normal and alcoholic subjects. [ABSTRACT FROM AUTHOR]

Background: The tracking devices could help measuring the heart rate and energy expenditure and recognizing the user's activity. The calorie measurement is a significant achievement for the fitness tracking and the continuous health monitoring. Methods: In this paper, a combination of an accelerometer and a photoplethysmography (PPG) sensor is implemented to calculate the calories consumed. These sensors were mounted next to each other and then were placed on the ankle and finger by flat cable. The sensed data are transferred via Bluetooth to a smartphone in a serial and real-time manner. An Android App is designed to display the user's health data. The average amount of consumed energy is obtained from the combination of the accelerometer sensor based on the laws of motion and the PPG sensor based on the heart rate data. Results: The designed system is tested on 10 nonathlete males and 10 nonathlete females randomly. By applying thevelet, the value of the acceleration signal variance was reduced from 3.2 to 0.8. The correlation between PPG and pulse oximeter was 0.9. Moreover, the correlation of the accelerometer and treadmill was 0.9. The root mean square error (RMSE) and the P value of the calorie output from PPG and pulse oximeter are 0.53 and 0.008, respectively. The RMSE and the P value of the calories output from the accelerometer and the treadmill are 0.42 and 0.007, respectively. Conclusion: Our device validity and reliability were good by comparing it with a typical smart band, smart watch, and smartphone available in the market. The combined PPG and the accelerometer sensors were compared with the gold standard, the pulse oximeter, and the treadmill. According to the results, there is no significant difference in the values obtained. Therefore, a mobile system is augmented with the wireless accelerometer and PPG that are connected to a smartphone. The system could be carried out with the user at any time and any place. [ABSTRACT FROM AUTHOR]

This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted with graph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume and with-in central 3 mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively. [ABSTRACT FROM AUTHOR]

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists. [ABSTRACT FROM AUTHOR]

In this paper, an efficient algorithm is proposed for detection of vertical root fractures (VRFs) in periapical (PA), and cone-beam computed tomography (CBCT) radiographs of nonendodontically treated premolar teeth. PA and CBCT images are divided into some sub-categories based on the fracture space between the two fragments as small, medium, and large for PAs and large for CBCTs. These graphics are first denoised using the combination of block matching 3-D filtering, and principle component analysis model. Then, we proposed an adaptive thresholding algorithm based on the modified Wellner model to segment the fracture and canal. Finally, VRFs are identified with a high accuracy through applying continuous wavelet transform on the segmented radiographs and choosing the most optimal value for sub-images based on the lowest interclass variance. Performance of the proposed algorithm is evaluated utilizing the different tested criteria. Results illustrate that the range of specificity deviations for PA and CBCT radiographs are 99.69 ± 0.22 and 99.02 ± 0.77, respectively. Furthermore, the sensitivity changes from 61.90 to 77.39 in the case of PA and from 79.54 to 100 in the case of CBCT. Based on our statistical evaluation, the CBCT imaging has the better performance in comparison with PA ones, so this technique could be a useful tool for clinical applications in determining the VRFs. [ABSTRACT FROM AUTHOR]

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 L² 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. [ABSTRACT FROM AUTHOR]

The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3. [ABSTRACT FROM AUTHOR]

Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin-blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results. [ABSTRACT FROM AUTHOR]

Aorta is formed in a dynamic environment which gives rise to imbalances between many forces that tend to extend the diameter and length. Furthermore, internal forces tend to resist this extension. Impedance tomography can show this imbalance to stimulate the stenosis of aortic valve, growth of the elastic, collagen and to effectively reduce the stresses in the underlying tissue. In blood flow, auscultation noises occurred and in the echocardiography decrease in left ventricular ejection speed can be observed. In this paper, we have modeled an aorta based on anatomical studies to simulate natural, 20% and 30% stenosis as usual heart disease to early diagnosis. Valve deformation causes different impedance tomography in 3D mesh of aorta as blood pressure. Remodeling of aorta and its flow is found when a cylindrical slice of the fully retracted blood aorta is cut longitudinally through the wall. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is a popular and noninvasive tool frequently used in the diagnoses of liver diseases. A system to characterize normal, fatty and heterogeneous liver, using textural analysis of liver Ultrasound images, is proposed in this paper. The proposed approach is able to select the optimum regions of interest of the liver images. These optimum regions of interests are analyzed by two level wavelet packet transform to extract some statistical features, namely, median, standard deviation, and interquartile range. Discrimination between heterogeneous, fatty and normal livers is performed in a hierarchical approach in the classification stage. This stage, first, classifies focal and diffused livers and then distinguishes between fatty and normal ones. Support vector machine and k-nearest neighbor classifiers have been used to classify the images into three groups, and their performance is compared. The Support vector machine classifier outperformed the compared classifier, attaining an overall accuracy of 97.9%, with a sensitivity of 100%, 100% and 95.1% for the heterogeneous, fatty and normal class, respectively. The Acc obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists and experts in liver diseases interpretation. [ABSTRACT FROM AUTHOR]

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heartstatlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithmclassifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. [ABSTRACT FROM AUTHOR]

To monitor the patient behavior, data are collected from patient's body by a medical monitoring device so as to calculate the output using embedded software. Incorrect calculations may endanger the patient's life if the software fails to meet the patient's requirements. Accordingly, the veracity of the software behavior is a matter of concern in the medicine; moreover, the data collected from the patient's body are fuzzy. Some methods have already dealt with monitoring the medical monitoring devices; however, model based monitoring fuzzy computations of such devices have been addressed less. The present paper aims to present synthesizing a fuzzy Petri-net (FPN) model to verify behavior of a sample medical monitoring device called continuous infusion insulin (INS) because Petri-net (PN) is one of the formal and visual methods to verify the software's behavior. The device is worn by the diabetic patients and then the software calculates the INS dose and makes a decision for injection. The input and output of the infusion INS software are not crisp in the real world; therefore, we present them in fuzzy variables. Afterwards, we use FPN instead of clear PN to model the fuzzy variables. The paper follows three steps to synthesize an FPN to deal with verification of the infusion INS device: (1) Definition of fuzzy variables, (2) definition of fuzzy rules and (3) design of the FPN model to verify the software behavior. [ABSTRACT FROM AUTHOR]

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter. [ABSTRACT FROM AUTHOR]

Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model. [ABSTRACT FROM AUTHOR]

Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest. [ABSTRACT FROM AUTHOR]

High power, high frequency pulsed electric fields known as pulsed power (PP) has been applied recently in biology and medicine. However, little attention has been paid to investigate the application of pulse power in musculoskeletal system and its possible effect on functional behavior and biomechanical properties of bone tissue. This paper presents the first research investigating whether or not PP can be applied safely on bone tissue as a stimuli and what will be the possible effect of these signals on the characteristics of cortical bone by comparing the mechanical properties of this type of bone pre and post expose to PP and in comparison with the control samples. A positive buck-boost converter was applied to generate adjustable high voltage, high frequency pulses (up to 500 V and 10 kHz). The functional behavior of bone in response to pulse power excitation was elucidated by applying compressive loading until failure. The stiffness, failure stress (strength) and the total fracture energy (bone toughness) were determined as a measure of the main bone characteristics. Furthermore, an ultrasonic technique was applied to determine and comprise bone elasticity before and after pulse power stimulation. The elastic property of cortical bone samples appeared to remain unchanged following exposure to pulse power excitation for all three orthogonal directions obtained from ultrasonic technique and similarly from the compression test. Nevertheless, the compressive strength and toughness of bone samples were increased when they were exposed to 66 h of high power pulsed electromagnetic field compared to the control samples. As the toughness and the strength of the cortical bone tissue are directly associated with the quality and integrity of the collagen matrix whereas its stiffness is primarily related to bone mineral content these overall results may address that although, the pulse power stimulation can influence the arrangement or the quality of the collagen network causing the bone strength and toughness augmentation, it apparently did not affect the mineral phase of the cortical bone material. The results also confirmed that the indirect application of high power pulsed electric field at 500 V and 10 kHz through capacitive coupling method was safe and did not destroy the bone tissue construction. [ABSTRACT FROM AUTHOR]