Search results for: MRI brain images

Authors: John Gaber, Youssef Ahmed, Hossam A. Gabbar, Jing Ren

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Accidents at Nuclear Power Plants (NPPs) occur due to various factors, notable among them being poor safety management and poor safety culture. During abnormal situations, the likelihood of human error is many-fold higher due to the higher cognitive workload. The most common cause of human error and high cognitive workload is mental fatigue. Electroencephalography (EEG) is a method of gathering the electromagnetic waves emitted by a human brain. We propose a safety system by monitoring brainwaves for signs of mental fatigue using an EEG system. This requires an analysis of the mental model of the NPP operator, changes in brain wave power in response to certain stimuli, and the risk factors on mental fatigue and attention that NPP operators face when performing their tasks. We analyzed these factors and developed an EEG-based monitoring system, which aims to alert NPP operators when levels of mental fatigue and attention hinders their ability to maintain safety.

Keywords: brain imaging, EEG, power plant operator, psychology

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Authors: Enqing Chen, Jianbo Wang

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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.

Keywords: edge detection, NSCT, shift invariant, modulus maxima

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Authors: Sergey Kozlov, Juliya Kozlova

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Introduction: The increased attention of researchers to an explosive trauma around the world is associated with a constant renewal of military weapons and a significant increase in terrorist activities using explosive devices. Explosive wave is a well known damaging factor of explosion. The most sensitive to the action of explosive wave in the human body are the head brain, lungs, intestines, urine bladder. The severity of damage to these organs depends on the distance from the explosion epicenter to the object, the power of the explosion, presence of barriers, parameters of the body position, and the presence of protective clothing. One of the places where a shock wave acts, in human tissues and organs, is the vascular endothelial barrier, which suffers the greatest damage in the head brain and lungs. The objective of the study was to determine the pathomorphological changes of the head brain followed the action of explosive wave. Materials and methods of research: To achieve the purpose of the study, there have been studied 6 male corpses delivered to the morgue of Municipal Institution "Dnipropetrovsk regional forensic bureau" during 2014-2016 years. The cause of death of those killed was a military explosive injury. After a visual external assessment of the head brain, for histological study there was conducted the 1 x 1 x 1 cm/piece sampling from different parts of the head brain, i.e. the frontal, parietal, temporal, occipital sites, and also from the cerebellum, pons, medulla oblongata, thalamus, walls of the lateral ventricles, the bottom of the 4th ventricle. Pieces of the head brain were immersed in 10% formalin solution for 24 hours. After fixing, the paraffin blocks were made from the material using the standard method. Then, using a microtome, there were made sections of 4-6 micron thickness from paraffin blocks which then were stained with hematoxylin and eosin. Microscopic analysis was performed using a light microscope with x4, x10, x40 lenses. Results of the study: According to the results of our study, injuries of the head brain were divided into macroscopic and microscopic. Macroscopic injuries were marked according to the results of visual assessment of haemorrhages under the membranes and into the substance, their nature, and localisation, areas of softening. In the microscopic study, our attention was drawn to both vascular changes and those of neurons and glial cells. Microscopic qualitative analysis of histological sections of different parts of the head brain revealed a number of structural changes both at the cellular and tissue levels. Typical changes in most of the studied areas of the head brain included damages of the vascular system. The most characteristic microscopic sign was the separation of vascular walls from neuroglia with the formation of perivascular space. Along with this sign, wall fragmentation of these vessels, haemolysis of erythrocytes, formation of haemorrhages in the newly formed perivascular spaces were found. In addition to damages of the cerebrovascular system, destruction of the neurons, presence of oedema of the brain tissue were observed in the histological sections of the brain. On some sections, the head brain had a heterogeneous step-like or wave-like nature. Conclusions: The pathomorphological microscopic changes in the brain, identified in the study on the died of explosive traumas, can be used for diagnostic purposes in conjunction with other characteristic signs of explosive trauma in forensic and pathological studies. The complex of microscopic signs in the head brain, i.e. separation of blood vessel walls from neuroglia with the perivascular space formation, fragmentation of walls of these blood vessels, erythrocyte haemolysis, formation of haemorrhages in the newly formed perivascular spaces is the direct indication of explosive wave action.

Keywords: blast wave, neurotrauma, human, brain

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Authors: Jaspal Rana, Alkem Laboratories, Baddi, Himachal Pradesh, India Chitkara University, Punjab, India

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Oxidative stress has been identified as an underlying cause of ischemia-reperfusion (IR) related cognitive dysfunction and brain damage. Therefore, antioxidant based therapies to treat IR injury are being investigated. Allium cepa L. (onion) is used as culinary medicine and is documented to have marked antioxidant effects. Hence, the present study was designed to evaluate the effect of A. cepa outer scale extract (ACE) against IR induced cognition and biochemical deficit in mice. ACE was prepared by maceration with 70% methanol and fractionated into ethylacetate and aqueous fractions. Bilateral common carotid artery occlusion for 10 min followed by 24 h reperfusion was used to induce cerebral IR injury. Following IR injury, ACE (100 and 200 mg/kg) was administered orally to animals for 7 days once daily. Behavioral outcomes (memory and sensorimotor functions) were evaluated using Morris water maze and neurological severity score. Cerebral infarct size, brain thiobarbituric acid reactive species, reduced glutathione, and superoxide dismutase activity was also determined. Treatment with ACE significantly ameliorated IR mediated deterioration of memory and sensorimotor functions and rise in brain oxidative stress in animals. The results of the present investigation revealed that ACE improved functional outcomes after cerebral IR injury, which may be attributed to its antioxidant properties.

Keywords: stroke, neuroprotection, ischemia reperfusion, herbal drugs

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Authors: Huan Peng, Chenye Zeng, Zhao Wang

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Alzheimer’s disease (AD) is an age-related neurodegenerative disease that is a leading cause of dementia syndromes and has become a huge burden on society and families. The main pathological features of AD involve excessive deposition of β-amyloid (Aβ) and Tau proteins in the brain, resulting in loss of neurons, expansion of neuroinflammation, and cognitive dysfunction in patients. Researchers have found effective drugs to clear the brain of error-accumulating proteins or to slow the loss of neurons, but their direct administration has key bottlenecks such as single-drug limitation, rapid blood clearance rate, impenetrable blood-brain barrier (BBB), and poor ability to target tissues and cells. Therefore, we are committed to seeking a suitable and efficient delivery system. Inspired by the possibility that exosomes may be involved in the secretion and transport mechanism of many signaling molecules or proteins in the brain, exosomes have attracted extensive attention as natural nanoscale drug carriers. We selected exosomes derived from bone marrow mesenchymal stem cells (MSC-EXO) with low immunogenicity and exosomes derived from hippocampal neurons (HT22-EXO) that may have excellent homing ability to overcome the deficiencies of oral or injectable pathways and bypass the BBB through nasal administration and evaluated their delivery ability and effect on AD. First, MSC-EXO and HT22 cells were isolated and cultured, and MSCs were identified by microimaging and flow cytometry. Then MSC-EXO and HT22-EXO were obtained by gradient centrifugation and qEV SEC separation column, and a series of physicochemical characterization were performed by transmission electron microscope, western blot, nanoparticle tracking analysis and dynamic light scattering. Next, exosomes labeled with lipophilic fluorescent dye were administered to WT mice and APP/PS1 mice to obtain fluorescence images of various organs at different times. Finally, APP/PS1 mice were administered intranasally with two exosomes 20 times over 40 days and 20 μL each time. Behavioral analysis and pathological section analysis of the hippocampus were performed after the experiment. The results showed that MSC-EXO and HT22-EXO were successfully isolated and characterized, and they had good biocompatibility. MSC-EXO showed excellent brain enrichment in APP/PS1 mice after intranasal administration, could improve the neuronal damage and reduce inflammation levels in the hippocampus of APP/PS1 mice, and the improvement effect was significantly better than HT22-EXO. However, intranasal administration of the two exosomes did not cause depression and anxious-like phenotypes in APP/PS1 mice, nor significantly improved the short-term or spatial learning and memory ability of APP/PS1 mice, and had no significant effect on the content of Aβ plaques in the hippocampus, which also meant that MSC-EXO could use their own advantages in combination with other drugs to clear Aβ plaques. The possibility of realizing highly effective non-invasive synergistic treatment for AD provides new strategies and ideas for clinical research.

Keywords: Alzheimer’s disease, exosomes derived from mesenchymal stem cell, intranasal administration, therapy-carrier dual roles

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Authors: Noor Ul Huda, Mushtaq Ahmed, Nadia Mushtaq, Naila Sher, Rahmat Ali Khan

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Purpose: The green synthesis of AgNPs has been favored over chemical synthesis due to their distinctive properties such as high dispersion, surface-to-volume ratio, low toxicity, and easy preparation. In the present work, the biosynthesis of AgNPs (KE-AgNPs) was carried out in one step by using the traditionally used plant Kickxia elatine (KE) extract and then investigated its enzyme inhibiting activity against rat’s brain acetylcholinesterase (AChE) in vitro. Methods: KE-AgNPs were synthesized from 1mM AgNO₃ using KE extract and characterized by UV–spectroscopy, SEM, EDX, XRD, and FTIR analysis. Rat’s brain acetylcholinesterase (AChE) inhibition activity was evaluated by the standard protocol. Results: UV–spectrum at 416 nm confirmed the formation of KE-AgNPs. X-ray diffraction (XRD) pattern presented 2θ values corresponding to the crystalline nature of KE-AgNPs with an average size of 42.47nm. The scanning electron microscope (SEM) analysis confirmed the presence of spherical-shaped and huge density KE-AgNPs with a size of 50nm. Fourier transform infrared spectroscopy (FT-IR) suggested that the functional groups present in KE extract and on the surface of KE-AgNPs are responsible for the stability of biosynthesized NPs. Energy dispersive X-ray (EDX) displayed an intense sharp peak at 3.2 keV, presenting that Ag was the chief element with 61.67%. Both KE extract and KE-AgNPs showed good and potent anti-AChE activity, with higher inhibition potential at a concentration of 175 µg/ml. Statistical analysis showed that both KEE and AgNPs exhibited non-competitive type inhibition against AChE, i.e., Vmax decreased (34.17-68.64% and 22.29- 62.10%) in the concentration-dependent mode for KEE and KE-AgNPs respectively and while Km values remained constant. Conclusions: KEE and KE-AgNPs can be considered an inhibitor of rats’ brain AChE, and the synthesis of KE-AgNPs-based drugs can be used as a cheaper and alternative option against diseases such as Alzheimer’s disease.

Keywords: Kickxia elatine, AgNPs, brain homogenate, acetylcholinesterase, kinetics

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Authors: Firas Gerges, Frank Y. Shih

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Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Boukhatem Mohammed Belkaid, Lahdir Mourad

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In this paper, we propose a new encryption system for security issues medical images. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity, and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every new session of encryption, that will be used to encrypt each frame of the medical image basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, medical images, encryption, decryption, key, correlation

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Authors: Alireza Shayegan, Morteza Amirabadi

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The y tool was developed to quantitatively compare dose distributions, either measured or calculated. Before computing ɣ, the dose and distance scales of the two distributions, referred to as evaluated and reference, are re-normalized by dose and distance criteria, respectively. The re-normalization allows the dose distribution comparison to be conducted simultaneously along dose and distance axes. Several two-dimensional images were acquired using a Scanning Liquid Ionization Chamber EPID and Extended Dose Range (EDR2) films for regular and irregular radiation fields. The raw images were then converted into two-dimensional dose maps. Transitional and rotational manipulations were performed for images using Matlab software. As evaluated dose distribution maps, they were then compared with the corresponding original dose maps as the reference dose maps.

Keywords: energetic electron, gamma function, penumbra, Matlab software

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Authors: Ravinder Kaur

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Introduction Global cerebrovascular reactivity (CVR) mapping is a promising clinical assessment for stress-testing the brain using physiological challenges, such as CO₂, to elicit changes in perfusion. It enables real-time assessment of cerebrovascular integrity and health. Conventional imaging approaches solely use steady-state parameters, like cerebral blood flow (CBF), to evaluate the integrity of the resting parenchyma and can erroneously show a healthy brain at rest, despite the underlying pathogenesis in the presence of cerebrovascular disease. Conversely, coupling CO₂ inhalation with phase-contrast MRI neuroimaging interrogates the capacity of the vasculature to respond to changes under stress. It shows promise in providing prognostic value as a novel health marker to measure neurovascular function in disease and to detect early brain vasculature dysfunction. Objective This exploratory study was established to:(a) quantify the CBF response to CO₂ in hypocapnia and hypercapnia,(b) evaluate disparities in CVR between internal carotid (ICA) and vertebral artery (VA), and (c) assess sex-specific variation in CVR. Methodology Phase-contrast MRI was employed to measure the cerebrovascular reactivity to CO₂ (±10 mmHg). The respiratory interventions were presented using the prospectively end-tidal targeting RespirActTM Gen3 system. Post-processing and statistical analysis were conducted. Results In 9 young, healthy subjects, the CBF increased from hypocapnia to hypercapnia in all vessels (4.21±0.76 to 7.20±1.83 mL/sec in ICA, 1.36±0.55 to 2.33±1.31 mL/sec in VA, p < 0.05). The CVR was quantitatively higher in ICA than VA (slope of linear regression: 0.23 vs. 0.07 mL/sec/mmHg, p < 0.05). No statistically significant effect was observed in CVR between male and female (0.25 vs 0.20 mL/sec/mmHg in ICA, 0.09 vs 0.11 mL/sec/mmHg in VA, p > 0.05). Conclusions The principal finding in this investigation validated the modulation of CBF by CO₂. Moreover, it has indicated that regional heterogeneity in hemodynamic response exists in the brain. This study provides scope to standardize the quantification of CVR prior to its clinical translation.

Keywords: cerebrovascular disease, neuroimaging, phase contrast MRI, cerebrovascular reactivity, carbon dioxide

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Authors: Nour-Elhouda Lecheheb

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Destination image is one of the main factors that influence potential visitors' decision choice. This study aims to explore the pre-visit perception of prior British tourists and compare them to the actual projected images of the Algerian tourism suppliers. Semi-structured interviews are conducted with both prior British tourists to Algeria and the Algerian tourism suppliers in 2019. The findings of this study suggest how the Algerian tourism suppliers might benefit from understanding the perceived image of prior tourists to match tourists' expectations and better plan their projected images.

Keywords: Algeria, destination choice, destination image, perceived image, projected image

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Authors: B.Shukir, H.Woo, P.Barzo, D.Kis

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Preoperative brain mapping in tumors involving the speech areas has an important role to reduce surgical risks. Functional magnetic resonance imaging (fMRI) is the gold standard method to localize cortical speech areas preoperatively, but its availability in clinical routine is difficult. Diffusion MRI based probabilistic tractography is available in head MRI. It’s used to segment cortical subregions by their structural connectivity. In our study, we used probabilistic tractography to localize the frontal and temporal cortical speech areas. 15 patients with left frontal tumor were enrolled to our study. Speech fMRI and diffusion MRI acquired preoperatively. The standard automated anatomical labelling atlas 3 (AAL3) cortical atlas used to define 76 left frontal and 118 left temporal potential speech areas. 4 types of tractography were run according to the structural connection of these regions to the left arcuate fascicle (FA) to localize those cortical areas which have speech functions: 1, frontal through FA; 2, frontal with FA; 3, temporal to FA; 4, temporal with FA connections were determined. Thresholds of 1%, 5%, 10% and 15% applied. At each level, the number of affected frontal and temporal regions by fMRI and tractography were defined, the sensitivity and specificity were calculated. At the level of 1% threshold showed the best results. Sensitivity was 61,631,4% and 67,1523,12%, specificity was 87,210,4% and 75,611,37% for frontal and temporal regions, respectively. From our study, we conclude that probabilistic tractography is a reliable preoperative technique to localize cortical speech areas. However, its results are not feasible that the neurosurgeon rely on during the operation.

Keywords: brain mapping, brain tumor, fMRI, probabilistic tractography

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Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

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Authors: Rishabh Ambavanekar

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Expressive Aphasia (EA) is an oral disability, common among stroke victims, in which the Broca’s area of the brain is damaged, interfering with verbal communication abilities. EA currently has no technological solutions and its only current viable solutions are inefficient or only available to the affluent. This prompts the need for an affordable, innovative solution to facilitate recovery and assist in speech generation. This project proposes a novel concept: using a wearable low-cost electroencephalography (EEG) device-based brain-computer interface (BCI) to translate a user’s inner dialogue into words. A low-cost EEG device was developed and found to be 10 to 100 times less expensive than any current EEG device on the market. As part of the BCI, a machine learning (ML) model was developed and trained using the EEG data. Two stages of testing were conducted to analyze the effectiveness of the device: a proof-of-concept and a final solution test. The proof-of-concept test demonstrated an average accuracy of above 90% and the final solution test demonstrated an average accuracy of above 75%. These two successful tests were used as a basis to demonstrate the viability of BCI research in developing lower-cost verbal communication devices. Additionally, the device proved to not only enable users to verbally communicate but has the potential to also assist in accelerated recovery from the disorder.

Keywords: neurotechnology, brain-computer interface, neuroscience, human-machine interface, BCI, HMI, aphasia, verbal disability, stroke, low-cost, machine learning, ML, image recognition, EEG, signal analysis

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Authors: Pomozova Kseniia, Gorlachev Gennadiy, Chernyaev Aleksandr, Golanov Andrey

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In clinical practice, and especially in stereotactic radiosurgery planning, the significance of diffusion-weighted imaging (DWI) is growing. This makes the existence of software capable of quickly processing and reliably visualizing diffusion data, as well as equipped with tools for their analysis in terms of different tasks. We are developing the «MRDiffusionImaging» software on the standard C++ language. The subject part has been moved to separate class libraries and can be used on various platforms. The user interface is Windows WPF (Windows Presentation Foundation), which is a technology for managing Windows applications with access to all components of the .NET 5 or .NET Framework platform ecosystem. One of the important features is the use of a declarative markup language, XAML (eXtensible Application Markup Language), with which you can conveniently create, initialize and set properties of objects with hierarchical relationships. Graphics are generated using the DirectX environment. The MRDiffusionImaging software package has been implemented for processing diffusion magnetic resonance imaging (dMRI), which allows loading and viewing images sorted by series. An algorithm for "masking" dMRI series based on T2-weighted images was developed using a deformable surface model to exclude tissues that are not related to the area of interest from the analysis. An algorithm of distortion correction using deformable image registration based on autocorrelation of local structure has been developed. Maximum voxel dimension was 1,03 ± 0,12 mm. In an elementary brain's volume, the diffusion tensor is geometrically interpreted using an ellipsoid, which is an isosurface of the probability density of a molecule's diffusion. For the first time, non-parametric intensity distributions, neighborhood correlations, and inhomogeneities are combined in one segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) algorithm. A tool for calculating the coefficient of average diffusion and fractional anisotropy has been created, on the basis of which it is possible to build quantitative maps for solving various clinical problems. Functionality has been created that allows clustering and segmenting images to individualize the clinical volume of radiation treatment and further assess the response (Median Dice Score = 0.963 ± 0,137). White matter tracts of the brain were visualized using two algorithms: deterministic (fiber assignment by continuous tracking) and probabilistic using the Hough transform. The proposed algorithms test candidate curves in the voxel, assigning to each one a score computed from the diffusion data, and then selects the curves with the highest scores as the potential anatomical connections. White matter fibers were visualized using a Hough transform tractography algorithm. In the context of functional radiosurgery, it is possible to reduce the irradiation volume of the internal capsule receiving 12 Gy from 0,402 cc to 0,254 cc. The «MRDiffusionImaging» will improve the efficiency and accuracy of diagnostics and stereotactic radiotherapy of intracranial pathology. We develop software with integrated, intuitive support for processing, analysis, and inclusion in the process of radiotherapy planning and evaluating its results.

Keywords: diffusion-weighted imaging, medical imaging, stereotactic radiosurgery, tractography

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Authors: Mariam Waheed Mekheimar

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Nascent research is conducted to the advancement of discourse analysis to include different modes as images, sound, and text. The focus of this study will be to elucidate how images are embedded with texts in an audio-visual medium as cinema to send political messages; it also seeks to broaden our understanding of politics beyond a relatively narrow conceptualization of the 'political' through studying non-traditional discourses as the cinematic discourse. The aim herein is to develop a systematic approach to film analysis to capture political meanings in films. The method adopted in this research is Multimodal Discourse Analysis (MDA) focusing on embedding visuals with texts. As today's era is the era of images and that necessitates analyzing images. Drawing on the writings of O'Halloran, Kress and Van Leuween, John Bateman and Janina Wildfeuer, different modalities will be studied to understand how those modes interact in the cinematic discourse. 'People at the top movie' is selected as an example to unravel the political meanings throughout film tackling the cinematic representation of the notion of social justice.

Keywords: Egyptian cinema, multimodal discourse analysis, people at the top, social justice

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Authors: Saeedeh Shahbazkhany

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Cancer is a terrible disease that, if not diagnosed early, therapy can be difficult while it is easily medicable if it is diagnosed in early stages. So it is very important for cancer diagnosis that medical procedures are performed. In this paper we introduce a new method. In this method, we only need pictures of healthy cells and cancer cells. In fact, where we suspect cancer, we take a picture of cells or tissue in that area, and then take some pictures of the surrounding tissues. Then, fractal dimension of images are calculated and compared. Cancer can be easily detected by comparing the fractal dimension of images. In this method, we use Matlab software.

Keywords: Matlab software, fractal dimension, cancer, surrounding tissues, cells or tissue, new method

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Authors: Mohammad Khazaei, Reza Rostami, Hasan Gharayagh Zandi, Rouhollah Basatnia, Mahbubeh Ghayour Najafabadi

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Objective: Methylphenidate has been demonstrated to enhance attention and cognitive processes, and placebo treatments have also been found to improve attention and cognitive processes. Additionally, methylphenidate may have positive effects on motion perception and sports performance. Nevertheless, additional research is needed to fully comprehend the neural mechanisms underlying the effects of methylphenidate and placebo on cognitive and motor functions. Methods: In this randomized controlled trial, 18 young semi-professional basketball players aged 18-23 years were randomly and equally assigned to either a Ritalin or Placebo group. The participants performed 20 consecutive free throws; their scores were recorded on a 0-3 scale. The participants’ brain activity was recorded using electroencephalography (EEG) for 5 minutes seated with their eyes closed. The Ritalin group received a 10 mg dose of methylphenidate, while the Placebo group received a 10mg dose of placebo. The EEG was obtained 90 minutes after the drug was administere Results: There was no significant difference in the absolute power of brain waves between the pre-test and post-tests in the Placebo group. However, in the Ritalin group, a significant difference in the absolute power of brain waves was observed in the Theta band (5-6 Hz) and Beta band (21-30 Hz) between pre- and post-tests in Fp2, F8, and Fp1. In these areas, the absolute power of Beta waves was higher during the post-test than during the pre-test. The Placebo group showed a more significant difference in free throw scores than the Ritalin group. Conclusions: In conclusion, these results suggest that Ritalin effect on brain activity in areas associated with attention and cognitive processes, as well as improve basketball free throws. However, there was no significant placebo effect on brain activity performance, but it significantly affected the improvement of free throws. Further research is needed to fully understand the effects of methylphenidate and placebo on cognitive and motor functions.

Keywords: methylphenidate, placebo effect, electroencephalography, basketball free throw

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Authors: Mariam G. Eshak, Ahmed Abbas, M. I. El-Sabry, M. M. Mashaly

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This investigation was conducted to determine the physiological response and evaluate the expression of inflammatory and cell death genes and DNA damage induced by endotoxic shock in laying hens. Endotoxic shock was induced by a single intravenous injection of 107 Escherichia coli (E. coli,) colony/hen. In the present study, 240 forty-week-old laying hens (H&N) were randomly assigned into 2 groups with 3 replicates of 40 birds each. Hens were reared in battery cages with wire floors in an open-sided housing system under natural conditions. Housing and general management practices were similar for all groups. At 42-wk of age, 45 hens from the first group (15 replicate) were infected with E. coli, while the same number of hens from the second group was injected with saline and served as a control. Heat shock protein-70 (HSP-70) expression, plasma corticosterone concentration, body temperature, and the gene expression of bax, caspase-3 activity, P38, Interlukin-1β (Il-1β), and tumor necrosis factor alpha (TNF-α) genes and DNA damage in the brain and liver were measured. Hens treated with E. coli showed significant (P≤0.05) increase of body temperature by 1.2 ᴼC and plasma corticosterone by 3 folds compared to the controls. Further, hens injected with E.Coli showed markedly over-expression of HSP-70 and increase DNA damage in brain and liver. These results were synchronized with activating cell death program since our data showed significant (P≤0.05) high expression of bax and caspase-3 activity genes in the brain and liver. These results were related to remarkable over-inflammation gene expression of P38, IL-1β, and TNF-α in brain and liver. In conclusion, our results indicate that endotoxic shock induces inflammatory physiological response and triggers cell death program by promoting P38, IL-1β, and TNF-α gene expression in the brain and liver.

Keywords: chicken, DNA damage, Escherichia coli, gene expression, inflammation

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Authors: Cristina Maria Ribeiro Martins Pereira Caridade, Ana Rita Ferreira Morais

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The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with healthcare facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool developed in MATLAB using a graphical user interface for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing you to view original images and fusion images, compare processed and original images, adjust parameters, and save images. The tool proposed in an innovative teaching and learning environment consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques and necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: image fusion, image processing, teaching-learning simulation tool, biomedical engineering education

Procedia PDF Downloads 93

Authors: Javaid Marium, Ahmad Iftikhar

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This study was aimed to identify factors that affect negative mood and body image dissatisfaction in women. positive and negative affect, self esteem, body image satisfaction and figure rating scale was administered to 97 female undergraduate students. This served as a base line data for correlation analysis in the first instance. One week later participants who volunteered to appear in the second phase of the study (N=47) were shown thin- ideal images as an intervention and soon after they completed positive and negative affect schedule and body image states scale again as a post test. Results indicated body mass as a strong negative predictor of body image dis/satisfaction, self esteem was a moderate predictor and mood was not a significant predictor. The participants whose actual body shape was markedly discrepant with the ideally desired body shape had significantly low level of body image satisfaction (p < .001) than those with low discrepancy. Similar results were found for self esteem (p < .004). Both self esteem and body mass predicted body satisfaction about equally and significantly. However, on viewing thin-ideal images, the participants of different body weight showed no change in their body image satisfaction than before. Only the overweight participants were significantly affected on negative mood as a short term reaction after viewing the thin ideal images. Comparing the three groups based on their body mass, one-way ANOVA revealed significant difference on negative mood as well as body image satisfaction. This reveals body mass as a potent and stable factor that consistently and strongly affected body satisfaction not the transient portrayal of thin ideal images.

Keywords: body image satisfaction, thin-ideal images, media, mood affects, self esteem

Procedia PDF Downloads 262

Authors: Igwenagu Chinelo Mercy

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This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

Procedia PDF Downloads 47

Authors: D. P. Gangwar

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Identification of human through facial images has got great importance in forensic science. The video recordings, CCTV footage, passports, driver licenses and other related documents are invariably sent to the laboratory for comparison of the questioned photographs as well as video recordings with suspected photographs/recordings to prove the identity of a person. More than 300 questioned and 300 control photographs received in actual crime cases, received from various investigation agencies, have been compared by me so far using various familiar analysis and comparison techniques such as Holistic comparison, Morphological analysis, Photo-anthropometry and superimposition. On the basis of findings obtained during the examination huge photo exhibits, a realistic and comprehensive technique has been proposed which could be very useful for forensic.

Keywords: CCTV Images, facial features, photo-anthropometry, superimposition

Procedia PDF Downloads 507

Authors: Javier Sedano-Cibrián, Julio Manuel de Luis-Ruiz, Rubén Pérez-Álvarez, Raúl Pereda-García, Beatriz Malagón-Picón

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Structural inspection activities are necessary to ensure the correct functioning of infrastructures. Unmanned Aerial Vehicle (UAV) techniques have become more popular than traditional techniques. Specifically, UAV Photogrammetry allows time and cost savings. The development of this technology has permitted the use of low-cost thermal sensors in UAVs. The representation of 3D thermal models with this type of equipment is in continuous evolution. The direct processing of thermal images usually leads to errors and inaccurate results. A methodology is proposed for the generation of 3D thermal models using dual sensors, which involves the application of visible Red-Blue-Green (RGB) and thermal images in parallel. Hence, the RGB images are used as the basis for the generation of the model geometry, and the thermal images are the source of the surface temperature information that is projected onto the model. Mining/industrial facilities representations that are obtained can be used for inspection activities.

Keywords: aerial thermography, data processing, drone, low-cost, point cloud

Procedia PDF Downloads 116

Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

Procedia PDF Downloads 108

Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: brain activity, EEG, information processing model, model human processor

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Authors: Simon L. Madsen, Mads Dyrmann, Morten S. Laursen, Rasmus N. Jørgensen

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Recent advances in the Unmanned Aerial System (UAS) safety and perception systems enable safe low altitude autonomous terrain following flights recently demonstrated by the consumer DJI Mavic PRO and Phamtom 4 Pro drones. This paper presents the first prototype system utilizing this functionality in form of semi-automated UAS based collection of crop/weed images where the embedded perception system ensures a significantly safer and faster gathering of weed images with sub-millimeter resolution. The system is to be used when the weeds are at cotyledon stage and prior to the harvest recognizing the grass weed species, which cannot be discriminated at the cotyledon stage.

Keywords: weed mapping, UAV, DJI SDK, automation, cotyledon plants

Procedia PDF Downloads 286

Authors: R. Sumalatha, M. V. Subramanyam

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In medical image processing the quality of the image is degraded in the presence of noise. Especially in ultra sound imaging and Magnetic resonance imaging the data was corrupted by signal dependent noise known as salt and pepper noise. Removal of noise from the medical images is a critical issue for researchers. In this paper, a new type of technique Adaptive Spatial Mask Filter (ASMF) has been proposed. The proposed filter is used to increase the quality of MRI and ultra sound images. Experimental results show that the proposed filter outperforms the implementation of mean, median, adaptive median filters in terms of MSE and PSNR.

Keywords: salt and pepper noise, ASMF, PSNR, MSE

Procedia PDF Downloads 413