Search results for: MR image of brain

Authors: Muna F. Al-Sammaraie

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The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform.

Keywords: image enhancement, multi-spectral, RGB, histogram

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Authors: Hee-Choul Kwon, Hyungjin Cho, Heeyong Kwon

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Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones.

Keywords: high speed rotation operation, image processing, image rotation, pattern recognition, transformation matrix

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Authors: Hee-Choul Kwon, Heeyong Kwon

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Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

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Authors: Grishma D. Solanki, Karshan Kandoriya

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In modern era of information age, digitalization has revolutionized like never before. Powerful computers, advanced photo editing software packages and high resolution capturing devices have made manipulation of digital images incredibly easy. As per as image forensics concerns, one of the most actively researched area are detection of copy move forgeries. Higher computational complexity is one of the major component of existing techniques to detect such tampering. Moreover, copy move forgery is usually performed in three steps. First, copying of a region in an image then pasting the same one in the same respective image and finally doing some post-processing like rotation, scaling, shift, noise, etc. Consequently, pseudo Zernike moment is used as a features extraction method for matching image blocks and as a primary factor on which performance of detection algorithms depends.

Keywords: copy-move image forgery, digital forensics, image forensics, image forgery

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Authors: S. Pesina, T. Solonchak

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An analysis of word semantics focusing on the invariance of advanced imagery in several pressing problems. Interest in the language of imagery is caused by the introduction, in the linguistics sphere, of a new paradigm, the center of which is the personality of the speaker (the subject of the language). Particularly noteworthy is the question of the place of the image when discussing the lexical, phraseological values and the relationship of imagery and metaphors. In part, the formation of a metaphor, as an interaction between two intellective entities, occurs at a cognitive level, and it is the category of the image, having cognitive roots, which aides in the correct interpretation of the results of this process on the lexical-semantic level.

Keywords: image, metaphor, concept, creation of a metaphor, cognitive linguistics, erased image, vivid image

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Authors: Farhad Forouharmajd, Hossein Ebrahimi, Siamak Pourabdian

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Introduction: Prevalent use of cell phones (mobile phones) has led to increasing worries about the effect of radiofrequency waves on the physiology of human body. This study was done to determine different reactions of the temperatures in different depths of brain tissue in confronting with radiofrequency waves of cell phones. Methodology: This study was an empirical research. A cow's brain tissue was placed in a compartment and the effects of radiofrequency waves of the cell phone was analyzed during confrontation and after confrontation, in three different depths of 2, 12, and 22 mm of the tissue, in 4 mm and 4 cm distances of the tissue to a cell phone, for 15 min. Lutron thermometer was used to measure the tissue temperatures. Data analysis was done by Lutron software. Findings: The rate of increasing the temperature at the depth of 22 mm was higher than 2 mm and 12mm depths, during confrontation of the brain tissue at the distance of 4 mm with the cell phone, such that the tissue temperatures at 2, 12, and 22 mm depths increased by 0.29 ˚C, 0.31 ˚C, and 0.37 ˚C, respectively, relative to the base temperature (tissue temperature before confrontation). Moreover, the temperature of brain tissue at the distance of 4 cm by increasing the tissue depth was more than other depths. Increasing the tissue temperature also existed by increasing the brain tissue depth after the confrontation with the cell phone. The temperature of the 22 mm depth increased with higher speed at the time confrontation. Conclusion: Not only radiofrequency waves of cell phones increased the tissue temperature in all the depths of the brain tissue, but also the temperature due to radiofrequency waves of the cell phone was more at the depths higher than 22 mm of the tissue. In fact, the thermal effect of radiofrequency waves was higher in higher depths.

Keywords: mobile phone, radio frequency waves, brain tissue, temperature

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: U.B Mahadevswamy UBM, Siraj Ahmed Siraj

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As the title of the paper indicates about brainwaves and its uses for various applications based on their frequencies and different parameters which can be implemented as real time application with the title a smart brain wave sensor system for paralyzed patients. Brain wave sensing is to detect a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brainwaves sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment. Keywords:-Brainwave sensor, BMI, Brain scan, EEG, MCH.

Keywords: Keywords:-Brainwave sensor , BMI, Brain scan, EEG, MCH

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Authors: Ramón Aparicio-García, Gustavo Juárez Gracia, Jesús Álvarez Cedillo

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A research line of the computer science that involve the study of the Human-Computer Interaction (HCI), which search to recognize and interpret the user intent by the storage and the subsequent analysis of the electrical signals of the brain, for using them in the control of electronic devices. On the other hand, the affective computing research applies the human emotions in the HCI process helping to reduce the user frustration. This paper shows the results obtained during the hardware and software development of a Brain Computer Interface (BCI) capable of recognizing the human emotions through the association of the brain electrical activity patterns. The hardware involves the sensing stage and analogical-digital conversion. The interface software involves algorithms for pre-processing of the signal in time and frequency analysis and the classification of patterns associated with the electrical brain activity. The methods used for the analysis and classification of the signal have been tested separately, by using a database that is accessible to the public, besides to a comparison among classifiers in order to know the best performing.

Keywords: affective computing, interface, brain, intelligent interaction

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

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Authors: Bilal Ahmad

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Alzheimer’s disease (AD) is one of the most common neurodegenerative illnesses. However, how Gut-microbiota plays a role in the pathogenesis of AD is not well elucidated. The purpose of this literature review is to summarize and understand the current findings that may elucidate the gut microbiota's role in the development of AD. Methods: A literature review of all the relevant papers known to the author was conducted. Relevant articles, abstracts and research papers were collected from well-accepted web sources like PubMed, PMC, and Google Scholar. Results: Recent studies have shown that Gut-microbiota has an important role in the progression of AD via Gut-Microbiota-Brain Axis. The onset of AD supports the ‘Hygiene Hypothesis’, which shows that AD might begin in the Gut, causing dysbiosis, which interferes with the intestinal barrier by releasing pro-inflammatory cytokines and making its way up to the brain via the blood-brain barrier (BBB). Molecular mechanisms lipopolysaccharides and serotonin kynurenine (tryptophan) pathways have a direct association with inflammation, the immune system, neurodegeneration, and AD. Conclusion: The studies helped to analyze the molecular basis of AD, other neurological conditions like depression, autism, and Parkinson's disease and how they are linked to Gut-microbiota. Further, studies to explore the therapeutic effects of probiotics in AD and cognitive enhancement should be warranted to provide significant clinical and practical value.

Keywords: gut-microbiota, Alzheimer’s disease, second brain aging, lipopolysaccharides, short-chain fatty acids

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Authors: Seung Ju Yeon, Seul Ki Min, Jun Sang Park, Yeo Seon Kwon, Hoo Cheol Lee, Hyun Jung Shim, Il-Doo Kim, Ja Kyeong Lee, Hwa Sung Shin

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In brain injury, depleting oxidative stress is the most effective way to reduce the brain infarct size. C-phycocyanin (C-Pc) is a well-known antioxidant protein that has neuroprotective effects obtained from green microalgae. Astrocyte is glial cell that supports the nerve cell such as neuron, which account for a large portion of the brain. In brain injury, such as ischemia and reperfusion, astrocyte has an important rule that overcomes the oxidative stress and protect from brain reactive oxygen species (ROS) injury. However little is known about how C-Pc regulates the anti-oxidants effects of astrocyte. In this study, when the C-Pc was treated in oxidized astrocyte, we confirmed that inflammatory factors Interleukin-6 and Interleukin-3 were increased and antioxidants enzyme, Superoxide dismutase (SOD) and catalase was upregulated, and neurotrophic factors, brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) was alleviated. Also, it was confirmed to reduce infarct size of the brain in ischemia and reperfusion because C-Pc has anti-oxidant effects in middle cerebral artery occlusion (MCAO) animal model. These results show that C-Pc can help astrocytes lead neuroprotective activities in the oxidative stressed environment of the brain. In summary, the C-PC protects astrocytes from oxidative stress and has anti-oxidative, anti-inflammatory, neurotrophic effects under ischemic situations.

Keywords: c-phycocyanin, astrocyte, reactive oxygen species, ischemia and reperfusion, neuroprotective effect

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Authors: Firoozeh Ghazanfari, Kourosh Amraei, Parisa Poorabadi

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Mild cognitive impairment is one of the neurocognitive disorders that go beyond age-related decline in cognitive functions, but in fact, it is not so severe which affects daily activities. This study aimed to investigate and compare the effectiveness of treatment with cranial electrical stimulation, brain training and their double combination on the language and verbal fluency of the elderly with mild cognitive impairment. This is a single-subject method with comparative intervention designs. Four patients with a definitive diagnosis of mild cognitive impairment by a psychiatrist were selected via purposive and convenience sampling method. Addenbrooke's Cognitive Examination Scale (2017) was used to assess language and verbal fluency. Two groups were formed with different order of cranial electrical stimulation treatment, brain training by pencil and paper method and their double combination, and two patients were randomly replaced in each group. The arrangement of the first group included cranial electrical stimulation, brain training, double combination and the second group included double combination, cranial electrical stimulation and brain training, respectively. Treatment plan included: A1, B, A2, C, A3, D, A4, where electrical stimulation treatment was given in ten 30-minutes sessions (5 mA and frequency of 0.5-500 Hz) and brain training in ten 30-minutes sessions. Each baseline lasted four weeks. Patients in first group who first received cranial electrical stimulation treatment showed a higher percentage of improvement in the language and verbal fluency subscale of Addenbrooke's Cognitive Examination in comparison to patients of the second group. Based on the results, it seems that cranial electrical stimulation with its effect on neurotransmitters and brain blood flow, especially in the brain stem, may prepare the brain at the neurochemical and molecular level for a better effectiveness of brain training at the behavioral level, and the selective treatment of electrical stimulation solitude in the first place may be more effective than combining it with paper-pencil brain training.

Keywords: cranial electrical stimulation, treatment, brain training, verbal fluency, cognitive impairment

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Prakash K. Aithal, U. Dinesh Acharya, Rajesh Gopakumar

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Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel .The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. The proposed method achieves pixel level parallelism as well as image level parallelism.

Keywords: edge detection, multicore, gpu, opencl, mpi

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Authors: Behnoush Sabayan, Ali Alidadi, Saeid Ebarhimi, N. M. Bakhshani

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Hemodialysis (HD) patients are at a high risk of atherosclerotic and vascular disease; also little information is available for the HD impact on brain structure of these patients. We studied the brain abnormalities in HD patients. The aim of this study was to investigate the effect of long term HD on brain structure of HD patients. Non-contrast MRI was used to evaluate imaging findings. Our study included 80 HD patients of whom 39 had less than six months of HD and 41 patients had a history of HD more than six months. The population had a mean age of 51.60 years old and 27.5% were female. According to study, HD patients who have been hemodialyzed for a long time (median time of HD was up to 4 years) had small vessel ischemia than the HD patients who underwent HD for a shorter term, which the median time was 3 to 5 months. Most of the small vessel ischemia was located in pre-ventricular, subcortical and white matter (1.33± .471, 1.23± .420 and 1.39±.490). However, the other brain damages like: central pons abnormality, global brain atrophy, thinning of corpus callosum and frontal lobe atrophy were found (P<0.01). The present study demonstrated that HD patients who were under HD for a longer time had small vessel ischemia and we conclude that this small vessel ischemia might be a causative mechanism of brain atrophy in chronic hemodialysis patients. However, additional researches are needed in this area.

Keywords: Hemodialysis Patients, Duration of Hemodialysis, MRI, Zahedan

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Authors: Panagodage Janaka Sampath Fernando

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Brain circulation has become a buzzword in the skilled migration literature. However, promoting brain circulation; returning of skilled migrants is challenging. Success stories in Asia, for instances, Taiwan, and China, are results of rigorous policy interventions of the respective governments. Nonetheless, the same policy mix has failed in other countries making it skeptical to attribute the success of brain circulation to the policy interventions per se. The paper seeks to answer whether the success of brain circulation within the Knowledge Services Industry (KSI) in Sri Lanka is a policy driven or a market driven process. Mixed method approach, which is a combination of case study and survey methods, was employed. Qualitative data derived from ten case studies of returned entrepreneurs whereas quantitative data generated from a self-administered survey of 205 returned skilled migrants (returned skilled employees and entrepreneurs) within KSI. The pull factors have driven the current flow of brain circulation within KSI but to a lesser extent, push factors also have influenced. The founding stone of the industry has been laid by a group of returned entrepreneurs, and the subsequent growth of the industry has attracted returning skilled employees. Sri Lankan government has not actively implemented the reverse brain drain model, however, has played a passive role by creating a peaceful and healthy environment for the industry. Therefore, in contrast to the other stories, brain circulation within KSI has emerged as a market driven process with minimal government interventions. Entrepreneurs play the main role in a market-driven process of brain circulation, and it is free from the inherent limitations of the reverse brain drain model such as discriminating non-migrants and generating a sudden flow of low-skilled migrants. Thus, to experience a successful brain circulation, developing countries should promote returned entrepreneurs by creating opportunities in knowledge-based industries.

Keywords: brain circulation, knowledge services industry, return migration, Sri Lanka

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Authors: Eman A. Al-Hilo, Hawraa H. Al-Waelly

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In this work, fractal compression (FIC) technique is introduced based on using moment features to block indexing the zero-mean range-domain blocks. The moment features have been used to speed up the IFS-matching stage. Its moments ratio descriptor is used to filter the domain blocks and keep only the blocks that are suitable to be IFS matched with tested range block. The results of tests conducted on Lena picture and Cat picture (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time (0.89 sec for Lena image and 0.78 of Cat image) with appropriate PSNR (30.01dB for Lena image and 29.8 of Cat image). The reduction in ET is about 12% for Lena and 67% for Cat image.

Keywords: fractal gray level image, fractal compression technique, iterated function system, moments feature, zero-mean range-domain block

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Gurinder Singh, Kulbir Singh

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Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques.

Keywords: Computer Forensics, Multimedia Forensics, Image Ballistics, Camera Source Identification, Forgery Detection

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Authors: Shahan Waheed, Muhammad Waqas, Asher Feroz

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Objectives: To determine the utility of base deficit in traumatic brain injury in assessing the severity and to correlate with the conventional computed tomography scales in grading the severity of head injury. Methodology: Observational cross-sectional study conducted in a tertiary care facility from 1st January 2010 to 31st December 2012. All patients with isolated traumatic brain injury presenting within 24 hours of the injury to the emergency department were included in the study. Initial Glasgow Coma Scale and base deficit values were taken at presentation, the patients were followed during their hospital stay and CT scan brain findings were recorded and graded as per the Rotterdam scale, the findings were cross-checked by a radiologist, Glasgow Outcome Scale was taken on last follow up. Outcomes were dichotomized into favorable and unfavorable outcomes. Continuous variables with normal and non-normal distributions are reported as mean ± SD. Categorical variables are presented as frequencies and percentages. Relationship of the base deficit with GCS, GOS, CT scan brain and length of stay was calculated using Spearman`s correlation. Results: 154 patients were enrolled in the study. Mean age of the patients were 30 years and 137 were males. The severity of brain injuries as per the GCS was 34 moderate and 109 severe respectively. 34 percent of the total has an unfavorable outcome with a mean of 18±14. The correlation was significant at the 0.01 level with GCS on presentation and the base deficit 0.004. The correlation was not significant between the Rotterdam CT scan brain findings, length of stay and the base deficit. Conclusion: The base deficit was found to be a good predictor of severity of brain injury. There was no association of the severity of injuries on the CT scan brain as per the Rotterdam scale and the base deficit. Further studies with large sample size are needed to further evaluate the associations.

Keywords: base deficit, traumatic brain injury, Rotterdam, GCS

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Authors: Roza Afarin, Saeed Mozaffari

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The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.

Keywords: correlation coefficients, genetic algorithm, image encryption, image entropy

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Authors: R. K. Pateriya, Jyoti Bharti

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This paper presents an approach for data hiding methods which provides a secret communication between sender and receiver. The data is hidden in gray-scale images and the boundary of gray-scale image is used to store the mapping information. In this an approach data is in ASCII format and the mapping is in between ASCII value of hidden message and pixel value of cover image, since pixel value of an image as well as ASCII value is in range of 0 to 255 and this mapping information is occupying only 1 bit per character of hidden message as compared to 8 bit per character thus maintaining good quality of stego image.

Keywords: ASCII value, cover image, PSNR, pixel value, stego image, secret message

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Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Ardiansyah I. Ryan, Ashsholih K. R., Fathurrohman G. R., Kurniadi M. R., Huda P. A

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The burden of brain-related disease is very high. There are a lot of brain-related diseases with limited treatment result and thus raise the burden more. The Parkinson Disease (PD), Mental Health Problem, or Paralysis of extremities treatments had risen concern, as the patients for those diseases usually had a low quality of life and low chance to recover fully. There are also many other brain or related neural diseases with the similar condition, mainly the treatments for those conditions are still limited as our understanding of the brain function is insufficient. Brain Implant Technology had given hope to help in treating this condition. In this paper, we examine the current update of the brain implant technology. Neurotechnology is growing very rapidly worldwide. The United States Food and Drug Administration (FDA) has approved the use of Deep Brain Stimulation (DBS) as a brain implant in humans. As for neural implant both the cochlear implant and retinal implant are approved by FDA too. All of them had shown a promising result. DBS worked by stimulating a specific region in the brain with electricity. This device is planted surgically into a very specific region of the brain. This device consists of 3 main parts: Lead (thin wire inserted into the brain), neurostimulator (pacemaker-like device, planted surgically in the chest) and an external controller (to turn on/off the device by patient/programmer). FDA had approved DBS for the treatment of PD, Pain Management, Epilepsy and Obsessive Compulsive Disorder (OCD). The target treatment of DBS in PD is to reduce the tremor and dystonia symptoms. DBS has been showing the promising result in animal and limited human trial for other conditions such as Alzheimer, Mental Health Problem (Major Depression, Tourette Syndrome), etc. Every surgery has risks of complications, although in DBS the chance is very low. DBS itself had a very satisfying result as long as the subject criteria to be implanted this device based on indication and strictly selection. Other than DBS, there are several brain implant devices that still under development. It was included (not limited to) implant to treat paralysis (In Spinal Cord Injury/Amyotrophic Lateral Sclerosis), enhance brain memory, reduce obesity, treat mental health problem and treat epilepsy. The potential of neurotechnology is unlimited. When brain function and brain implant were fully developed, it may be one of the major breakthroughs in human history like when human find ‘fire’ for the first time. Support from every sector for further research is very needed to develop and unveil the true potential of this technology.

Keywords: brain implant, deep brain stimulation (DBS), deep brain stimulation, Parkinson

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Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

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Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

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Authors: Mostafa El Khashab

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Our experience with 50 patients with metastatic tumors located in different locations of the brain by a stereotactic-guided craniotomy and total microsurgical resection. Patients ranged in age from 36 to 73 years. There were 28 women and 22 men. Thirty-four patients presented with hemiparesis and 6 with aphasia and the remaining presented with psychological manifestations and memory issues. Gross total resection was accomplished in all cases, with postoperative imaging confirmation of complete removal. Forty patients were subjected to whole brain irradiation. One patient developed a stroke postoperatively and another one had a flap infection. 4 patients developed different postoperative but unrelated morbidities, including pneumonia and DVT. No mortality was encountered. We believe that with the assistance of stereotactic localization, metastases in vital regions of the brain can be removed with very low neurologic morbidity and that, in comparison to other modalities, they fare better regarding their long-term outcome.

Keywords: stereotactic, craniotomy, radiosurgery, patient

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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

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Authors: Arezou Hajhashemi, Karim Asgari, Masoud Etemadifar, Maryam Keyvani, Ali Hekmatnia

Abstract:

Multiple Sclerosis (MS) is one of the most prevalent demyelinating diseases in CNS. As in other chronic cerebral diseases, impairment in cognitive functioning and in memory is popular. Because of the inflammatory and demyelinating nature of the disease, the localization of plaques in different parts of the Prefrontal and Limbic System, may lead to memorial symptoms. This investigation was intended to study relationship between frequency of plaques and memorial symptoms arising from dysfunction limbic system and prefrontal of patients with MS. The sample was selected randomly from patients with MS with memory problem, who have been referred to Isfahan Multiple Sclerosis Society. Brain System Test and Memory Test was administered to the sample, and their MRI's were analyzed by specialist in order to indentify two different parts of plaques. The data was analyzed by SPSS. The results showed that there were significant relationship between MS plaques and prefrontal's dysfunction and memorial symptom related to prefrontal area; however, there were no significant relationship between MS plaques and limbic system's dysfunction and memorial symptoms related to limbic system area. The results of this study suggest that memorial symptoms due to injury regions of the brain have the most significant relationship to prefrontal. Better judgment about these results needs more studies in future.

Keywords: multiple sclerosis, magnetic image, brain injury, behavior disorder

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

Abstract:

Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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