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   Table of Contents - Current issue
April-June 2019
Volume 9 | Issue 2
Page Nos. 77-144

Online since Monday, June 24, 2019

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Providing a four-layer method based on deep belief network to improve emotion recognition in electroencephalography in brain signals p. 77
Seyed Mohammad Reza Mousavinasr, Ali Pourmohammad, Mohammad Sadegh Moayed Saffari
DOI:10.4103/jmss.JMSS_34_17  PMID:31316901
Background: One of the fields of research in recent years that has been under focused is emotion recognition in electroencephalography (EEG) signals. This study provides a four-layer method to improve people's emotion recognition through these signals and deep belief neural networks. Methods: In this study, using DEAP dataset, a four-layer method is established, which includes (1) preprocessing, (2) extracting features, (3) dimension reduction, and (4) emotion identification and estimation. To find the optimal choice in some of the steps of these layers, three different tests have been conducted. The first is finding the perfect window in feature extraction section that resulted in superiority of Hamming window to the other windows. The second is choosing the most appropriate number of filter bank and the best result was 26. The third test was also emotion recognition that its accuracy was 92.93 for arousal dimension, 92.64 for valence dimension, 93.14 for dominance dimension in two-class experiment and 76.28 for the arousal, 74.83 for the valence, and 75.64 for dominance in three-class experiment. Results: The results of this method show an improvement of 12.34% and 7.74% in two- and three-class levels in the arousal dimension. This improvement in the valence is 12.77 and 8.52, respectively. Conclusion: The results show that the proposed method can be used to improve the accuracy of emotion recognition.
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Objective assessment of skin repigmentation using a multilayer perceptron p. 88
Juan Fernando Chica, Sayonara Zaputt, Javier Encalada, Christian Salamea, Melissa Montalvo
DOI:10.4103/jmss.JMSS_52_18  PMID:31316902
Background: Vitiligo is a pathology that causes the appearance of achromic macules on the skin that can spread on to other areas of the body. It is estimated that it affects 1.2% of the world population and can disrupt the mental state of people in whom this disease has developed, generating negative feelings that can become suicidal in the worst of cases. The present work focuses on the development of a support tool that allows to objectively quantifying the repigmentation of the skin. Methods: We propose a novel method based on artificial neural networks that use characteristics of the interaction of light with the skin to determine areas of healthy skin and skin with vitiligo. We used photographs of specific areas of skin containing vitiligo. We select as independent variables: the type of skin, the amount of skin with vitiligo and the amount of repigmented skin. Considering these variables, the experiments were organized in an orthogonal table. We analyzed the result of the method based on three parameters (sensitivity, specificity, and F1-Score) and finally, its results were compared with other methods proposed in similar research. Results: The proposed method demonstrated the best performance of the three methods, and it also showed its capability to detect healthy skin and skin with vitiligo in areas up to 1 × 1 pixels. Conclusion: The results show that the proposed method has the potential to be used in clinical applications. It should be noted that the performance could be significantly improved by increasing the training patterns.
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Nonlinear analysis of electroencephalogram signals while listening to the holy Quran p. 100
Mahsa Vaghefi, Ali Motie Nasrabadi, Seyed Mohammad Reza Hashemi Golpayegani, Mohammad Reza Mohammadi, Shahriar Gharibzadeh
DOI:10.4103/jmss.JMSS_37_18  PMID:31316903
Background: Electrical activity of the brain, resulting from electrochemical signaling between neurons, is recorded by electroencephalogram (EEG). The neural network has complex behavior at different levels that strongly confirms the nonlinear nature of interactions in the human brain. This study has been designed and implemented with the aim of determining the effects of religious beliefs and the effect of listening to Holy Quran on electrical activity of the brain of the Iranian Persian-speaking Muslim volunteers. Methods: The brain signals of 47 Persian-speaking Muslim volunteers while listening to the Holy Quran consciously, and while listening to the Holy Quran and the Arabic text unconsciously were used. Therefore, due to the nonlinear nature of EEG signals, these signals are studied using approximate entropy, sample entropy, Hurst exponent, and Detrended Fluctuation Analysis. Results: Statistical analysis of the results has shown that listening to the Holy Quran consciously increases approximate entropy and sample entropy, and decreases Hurst Exponent and Detrended Fluctuation Analysis compared to other cases. Conclusion: Consciously listening to the Holy Quran decreases self-similarity and correlation of brain signal and instead increases complexity and dynamicity in the brain.
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Evaluation of mechanical properties and cell viability of poly (3-hydroxybutyrate)-chitosan/Al2O3nanocomposite scaffold for cartilage tissue engineering p. 111
Elahe Bahremandi Toloue, Saeed Karbasi, Hossein Salehi, Mohammad Rafienia
DOI:10.4103/jmss.JMSS_56_18  PMID:31316904
Background: The aim of this study was to evaluate the effects of alumina nanowires as reinforcement phases in polyhydroxybutyrate-chitosan (PHB-CTS) scaffolds to apply in cartilage tissue engineering. Methods: A certain proportion of polymers and alumina was chosen. After optimization of electrospun parameters, PHB, PHB-CTS, and PHB-CTS/3% Al2O3nanocomposite scaffolds were randomly electrospun. Scanning electron microscopy, Fourier transform infrared spectroscopy, water contact angle measurement, tensile strength, and chondrocyte cell culture studies were used to evaluate the physical, mechanical, and biological properties of the scaffolds. Results: The average fiber diameter of scaffolds was 300–550 nm and the porosity percentages for the first layer of all types of scaffolds were more than 81%. Scaffolds' hydrophilicity was increased by adding alumina and CTS. The tensile strength of scaffolds decreased by adding CTS and increased up to more than 10 folds after adding alumina. Chondrocyte viability and proliferation on scaffolds were better after adding CTS and alumina to PHB. Conclusion: With regard to the results, electrospun PHB-CTS/3% Al2O3scaffold has the appropriate potential to apply in cartilage tissue engineering.
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Interpretation of in-air output ratio of wedged fields in different measurement conditions p. 117
Parinaz Mehnati, Farideh Biglari, Ali Jomehzadeh
DOI:10.4103/jmss.JMSS_36_18  PMID:31316905
Background: The collimator scatter factor (Sc) is one of the most important parameters in monitor unit (MU) calculation. There are several factors that impact Scvalues, including head structures, backscatter in dose monitoring chambers, and wedges. The objective of this study was to investigate the variation of Scwith different buildup cap materials, wall thickness of buildup caps, source-to-chamber distances (SCDs), ionization chambers, and wedge angles in 6 MV photon beam. Methods: In this study, copper and Perspex buildup caps were made with two different thicknesses for each buildup cap. Measurements were performed on an Elekta Compact medical linear accelerator (6 MV) using RK dosimeter with a sensitive volume of 0.120 cm3 and Farmer-type ion chamber with a sensitive volume of 0.65 cm3. In all measurements, buildup caps and ionization chambers were positioned such as to stand vertically to the beam central axis. It was also investigated the effect of internal wedge with different angles (30° and 60°) different SCDs on Sc. Results: It was found in large field sizes, Scvalues in Perspex buildup cap were higher than copper. Different SCDs and type of ion chamber and wall thickness of buildup caps had no significant influence on Scvalues. The presence of wedge influenced Scvalues significantly. Variation of Scin wedged fields compared to open fields had a maximum deviation of 0.9% and 6.8% in 30° and 60° wedge angles, respectively. Conclusion: It was found that the presence of wedges had a significant influence on Scand increases with wedge angles. As such, it should be taken into account in manual MU calculations.
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Introduction of a simple algorithm to create synthetic-Computed tomography of the head from magnetic resonance imaging p. 123
Nahid Chegeni, Mohamad Javad Tahmasebi Birgani, Fariba Farhadi Birgani, Daryoush Fatehi, Gholamreza Akbarizadeh, Marziyeh Tahmasbi
DOI:10.4103/jmss.JMSS_26_18  PMID:31316906
Background: Recently, magnetic resonance imaging (MRI)-based radiotherapy has become a favorite science field for treatment planning purposes. In this study, a simple algorithm was introduced to create synthetic computed tomography (sCT) of the head from MRI. Methods: A simple atlas-based method was proposed to create sCT images based on the paired T1/T2-weighted MRI and bone/brain window CT. Dataset included 10 patients with glioblastoma multiforme and 10 patients with other brain tumors. To generate a sCT image, first each MR from dataset was registered to the target-MR, the resulting transformation was applied to the corresponding CT to create the set of deformed CTs. Then, deformed-CTs were fused to generate a single sCT image. The sCT images were compared with the real CT images using geometric measures (mean absolute error [MAE] and dice similarity coefficient of bone [DSCbone]) and Hounsfield unit gamma-index (ГHU) with criteria 100 HU/2 mm. Results: The evaluations carried out by MAE, DSCbone, and ГHUshowed a good agreement between the synthetic and real CT images. The results represented the range of 78–93 HU and 0.80–0.89 for MAE and DSCbone, respectively. The ГHUalso showed that approximately 91%–93% of pixels fulfilled the criteria 100 HU/2 mm for brain tumors. Conclusion: This method showed that MR sequence (T1w or T2w) should be selected depending on the type of tumor. In addition, the brain window synthetic CTs are in better agreement with real CT relative to bone window sCT images.
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Drowsiness analysis using common spatial pattern and extreme learning machine based on electroencephalogram signal p. 130
Osmalina Nur Rahma, Akif Rahmatillah
DOI:10.4103/jmss.JMSS_54_18  PMID:31316907
An alarm system has become essential to prevent someone from drowsiness while driving, considering the high incidence due to fatigue or drowsiness. This study offered an alternative to overcome all the limitations provided by the conventional system to detect sleepiness based on the driver's brain electrical activity using wearable electroencephalogram (EEG), which is lighter and easy to use. The EEG signals were collected using EMOTIV Epoc + and then were decomposed into narrowband frequency, such as delta, theta, alpha, and beta using DWT. The relative power, as the result of feature extraction, then were processed further by calculating its variance using the common spatial pattern (CSP) method to optimize the accuracy of extreme learning machine (ELM). Comparison of relative power between awake and drowsy state showed that during the drowsy state, theta-wave, alpha-wave, and beta-wave were tend to be higher than in the awake state. However, despite with the help of ELM, the accuracy was not too high (below 87%). The feature extraction which continued by calculating its variance using CSP algorithm before classified by ELM obtained a high accuracy, even with small amount of data training. This showed that CSP combining with ELM could be useful to shorten the time in training/calibration session, yet still, obtained high accuracy in classifying the awake state and drowsy state. The overall average accuracy of testing ranged from 91.67% to 93.75%. This study could increase the ability of EEG in detecting drowsiness that is important to prevent the risk caused by driving in a drowsy state.
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Image quality assessment of the digital radiography units in Tabriz, Iran: A phantom study p. 137
Nahideh Gharehaghaji, Davood Khezerloo, Tohid Abbasiazar
DOI:10.4103/jmss.JMSS_30_18  PMID:31316908
Creating a high-quality image with the low patient dose is one of the most important goals in medical X-ray imaging. In this study, the image quality parameters of the digital radiographic units in Tabriz city were considered and compared with the international protocols. The image quality parameters were measured at 11 high workload digital radiography (DR) imaging centers in Tabriz city, and the results were compared to DINN 6868/58 standards. All centers equipped with the direct DR units passed the spatial resolution, low contrast detectability, contrast dynamic range, and noise tests, while the computed radiography (CR) units only could pass the two last tests. The highest spatial resolution was observed 3.2 lp/mm in the DR unit while the lowest one was 1.8 lp/mm in the CR unit. The highest noise was measured to be 0.03 OD that was observed in the DR unit. The most difference between the nominal and measured peak kilovoltage and mAs was 3.1% and 6.8%, respectively. The entrance surface air kerma in all units was obtained <0.63 mGy. The measured half-value layer range was between 2.4 and 3.54 mmAl. The physical parameters of image quality such as spatial resolution, contrast, and noise are robustness quantitative parameters for the assessment of the image quality performance of the units. Therefore, measurement and control of these parameters using two-dimensional phantoms are very critical.
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Retraction: Extraction of the best frames in coronary angiograms for diagnosis and analysis p. 143

DOI:10.4103/jmss.JMSS_27_19  PMID:31316909
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Retraction: Vesselness-guided Active Contour: A coronary vessel extraction method p. 144

DOI:10.4103/jmss.JMSS_28_19  PMID:31316910
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