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609 results for “brain imaging”
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
Figures a, b ,c -8-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>In turn, the optimized results of<br> GFCM provide overall accuracy of 95%. Figures 8(a), 8(b) and 8(c) shows the comparison results<br> of clustering models and optimization technique in terms of Se, Sp and Acc.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
Figure 6.Amygdala hypofunction after a single oral 40-mg dose, 1.5-hours post-dose, in young study participants.The image has been adapted from (Hurlemann et al., 2010).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In the past decade, there has been much interest in identifying treatment in adding to the<br> current treatment options for war veterans suffering from Post-Traumatic Stress Disorder (PTSD).<br> The studies investigating secondary-preventative measures for PTSD using Propranolol due to the<br> drug’s ability to inhibit the actions of the neurotransmitter norepinephrine,which has been<br> implicated to enhance the consolidation(McGhee et al., 2009; Pitman et al., 2002; Stein et al.,<br> 2007).Further, in a double-blind, placebo-controlled,functional Magnetic Resonance Imaging<br> (fMRI) study, in healthy volunteers, Hurlemann et al. found that a single oral 40mg dose of<br> propranolol attenuatedthe leftbasolateral amygdala responses to the face perception<br> paradigm(Hurlemann et al., 2010). The study participants were eighteen healthy (9 females, 9<br> males; mean age 23 years; age range 19–31 years) who had their fMRI acquisition 1.5-hours after<br> the oral administration of propranolol. An adapted image of the study findings are shown in Figure<br> 6.</p>
Figure 5.(a)Linear (y=0.45x + 57.74) dose-response relationship between plasma propranolol to % β- adrenergeric blockade derived from healthy study participants and translate into patients with angina pectoris. This image has been adapted from(Pine et al., 1975).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Apharmacodynamic model,with parameters in the table below, may be used to visualize the<br> propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris.<br> These results have been adapted from the Pine et al article published in Circulation in 1975 which<br> identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade<br> in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975).</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 11.Weka Results for Images
<p>The dataset of the real images is also treated the same way as the dataset of the sketches. Total tree images used = 86 Total car images used = 54 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 10 shows the neural network for images. The results are shown in Figure 11 and are as follows: Total Correct Recognition = 83.7838% Total Incorrect Recognition = 16.2162% Error Per epoch = 0.0235851 </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 8. Training Neural Network for Sketches
<p>It is described in the previous section that a mobile application is used to collect data about the sketches and the data is converted to 20x20=400 integer numbers to give it as input to Weka. Our experimentation includes only two objects for recognition i.e. trees and cars. Total tree sketches used = 175 Total car sketches used = 72 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 8 shows the neural network for sketches. The results are shown in Figure 9 and are as follows: Total Correct Recognition = 100% </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 5. Cropped Sketch
<p>Figure 5 shows the interface of the mobile application that is created to collect the data of the sketches. It contains a drawing canvas; where drawings are made and it also contains few text boxes which are filled with the information of the drawing. For example, if the drawing is a tree, ‘1’ is written in the tree text box. Furthermore, the undo and redo buttons help modify the drawing and the clear button clears everything drawn onto the drawing canvas. </p> <p>The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network. Therefore, there is implemented a crop function which crops the image very minutely and removes the empty space as shown in Figure 5. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 4. Mobile Application to collect Sketch data
<p>The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network.</p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 3. The used methodology
<p>The training process we use in both trainings is based on neural networks, using the backpropgation algorithm. By using this algorithm, we are going to finally obtain the weights that will be used in our model to make the system recognize the input given by the user. </p> <p>Once the training for the hand drawn sketches is over, we are going to get weights that will be used to recognize any inputted hand drawn sketches. For example, if the user draws a tree, the system will be able to recognize what has been drawn as a tree, using the obtained weights from the neural networks. On the other hand, once the training for the real images is over, it means that if we provide our system with a real image, for example a tree, the system will be able to recognize it using the obtained weights from the training phase. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 6. Reducing size of Sketch
<p>As previously described, there are a total of 1600 integers, which can be the input to the dataset for neural networks. But it is a huge number, so in order to minimize the size of inputs to the neural network the 40x40 matrix is reduced to 20x20 by skipping odd rows and columns of the original matrix. Figure 6 shows a matrix containing green and red rows and columns. If this was the 40x40 matrix, then the red part of this matrix would be skipped to convert it into a 20x20 sized matrix. Now there are only 20x20=400 values, which is a reasonable input size for the neural network. </p> <p>The other part of the developed approach is to collect the data about the same two objects of the real pictures taken by the camera. These images are converted into black and white pictures and then treated the same way as the sketches, i.e. black and white pictures are also converted into integers based on the color of each pixel. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 1. Methodology used in the research by (Egmont-Petersen et al., 2002)
<p>Figure 1 shows the methodology used by Egmont-Petersen et al. (2002) to come up with an answer to their research question. Their study says that image recognition using neural networks goes through the stages shown in Figure 1. In our research we shall use this general proposed approach. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 10. Training Neural Network for Images
<p>The dataset of the real images is also treated the same way as the dataset of the sketches. Total tree images used = 86 Total car images used = 54 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 10 shows the neural network for images. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 2. The used approach
<p>In this section we present in details the methodology used for this research. Figure 2 depicts the used methodology. The adopted approach is based on first training the system to be able to recognize certain sketches by providing it with various hand-drawn examples such as trees, cars and mountains. Secondly, the system has to be trained again to recognize real images by providing it with real images such as trees, cars and mountains. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 7. Cropping image
<p>This function takes five parameters, a WriteableBitmap type of object, the location of the starting point on x coordinate, the location of the starting point on y coordinate, width and height. It crops the image according to these parameters as shown in Figure 7. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 9. Weka Results for Sketches
<p> the sketches and the data is converted to 20x20=400 integer numbers to give it as input to Weka. Our experimentation includes only two objects for recognition i.e. trees and cars. Total tree sketches used = 175 Total car sketches used = 72 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 8 shows the neural network for sketches. The results are shown in Figure 9 and are as follows: Total Correct Recognition = 100% </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.