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609 results for “brain imaging”
A novel strategy for fully automated segmentation of supratentorial meningiomas: Use of pre-trained models and inclusion of normal brain images
<p>This repository is accompanying MRI datasets under the journal, titled: <strong>A novel strategy for fully automated segmentation of supratentorial meningiomas: Use of pre-trained models and inclusion of normal brain images</strong>. </p> <p>Nii_data.tar.gz (zipped) file includes MRI images of all patients described in the paper that are formatted as .nii.</p>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
<p>We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 7. Effect of different type of movement on the image in the vision of artificial creature.
<p>Figure 7a, Figure 7b, Figure 7c and Figure 7d, shows effect of different type of movement on the image in the vision of artificial creature if food be on vision boundaries. As mentioned each part of the image equal 7.5 degree.<br> Therefore 15 degree left or right rotation locomotion equivalent two parts shift toward left or right.<br> For motion to forward direction, size of the image has been reduplicated so that each part has been become to the two similar parts. Then half of new image in right side and left side has been deleted in order to create new close image in vision. Accordingly, if food be on vision boundaries, the number of black parts of the image for the food object in ultimate location is 2, by one movement to forward direction the number of these parts become to 4, by one movement to forward direction the number of these parts become to 8 and so on. After four movements to forward all part of the image is black and the creature is succeed find the food object.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 6, One image in sight of artificial creature.
<p>Figure 6 shows an image in sight of the creature, where the food in this image is the dark part of the vision. If the food object places in the vision edge of the artificial creature (2 meters), two squares in the image in sight of the creature becomes black and by getting the artificial creature closer to the food, more squares of image in sight become block and if all parts of the image in sight of the creature become black, the artificial creature has been successful in finding the food object. In Figure 6 each part of the image divided to 3 subparts because 3 neurons per part of image in the input layer of the artificial creature network (vision) have been considered. So vision of the artificial creature composed of 60 neurons due to:</p> <p>For each black part of image in sight of the artificial creature, three signals as the input signals is applied to the three respective neurons.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Principal stages of image classification system
<p>In computer vision, images or objects are recognised by machine going through two phases<br> shown in the figure 4. First the system is trained with features extracted from sample images in<br> training stage then they are tested on input images in testing stage. The performance of the classifier<br> depends on features extracted from the image. This research work is carried out in three different<br> experiments, first experiment is performed on data set containing the original images of sixteen<br> categories, noisy images are classified in second experiment, and third experiment detects the type<br> of noise affected the image followed by filtering through appropriate filter, then filtered images are<br> classified. The performance of each of the experiment is measured with two approaches. First<br> approach extracts the statistical texture features of the whole image, and the original image of size<br> 128x128 is divided into sixteen blocks of size 32x32 pixels in second approach. Then six statistical<br> texture features discussed in second section are extracted from each of the block producing 96<br> features from each of the images are used for training and testing stage.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 1. Sample Images of sixteen categories
<p>An image is often corrupted by noise in its acquisition or transmission. Noise is any<br> undesired information that degrades the image and appears in images from a variety of sources.</p> <p>Basically, there are three standard noise models [17], which model the types of noise<br> encountered in most images; they are additive noise, multiplicative noise and impulse noise. In this<br> work we have considered the occurrence of additive noise. An image function is given by f (x, y)<br> where (x, y) is spatial coordinate and f is intensity at point(x, y). Let f (x, y) be the original image,<br> g(x, y) be the noisy version and η(x, y) be the noise function, which returns random values coming<br> from an arbitrary distribution.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 7. Performance in experiment 3 for both approaches
<p>Classification of noisy images starts with detection of type of noise followed by appropriate<br> filtering operation. Then similar approaches are followed for feature extraction as discussed in<br> above experiments.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Feed Forward Back propagation Neural Network
<p>This BPNN provides a computationally efficient<br> method for changing the weights in feed forward network, with differentiable activation function<br> units, to learn a training set of input-output data. Being a gradient descent method it minimizes the<br> total squared error of the output computed by the net. The aim is to train the network to achieve a<br> balance between the ability to respond correctly to the input patterns that are used for training and<br> the ability to provide good response to the input that are similar. A typical back propagation<br> network of input layer, one hidden layer and output layer is shown in figure 4.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 6. Performance in experiment 2 for both approaches
<p>Second experiment is performed on noisy data set with 50 images each class for training of<br> feed forward neural network, and 100 images each class are used for testing phase. This experiment<br> is also carried out with two approaches as discussed in first experiment.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 5. Performance in experiment 1 for both approaches
<p>The first experiment is carried out on data set containing 16 class and 50 images of each<br> class for training and 100 images of each class for testing. Initially the original images are resized to<br> 128X128 pixels and six texture features are extracted from 800 training images in the first<br> approach. It produces a feature matrix of size 6X800. Similarly 6X1600 feature matrix is produced<br> for testing stage. Then a neural classifier is designed with ten hidden layers and 16 output layers.<br> Feature matrix of training images are used to train the neural network. After that they are tested on feature matrix of test image. In second approach, the original images are divided into 16 blocks of<br> size 32X32 pixel each block. So it produces 96 features (16 blocksX6 features) for each image and<br> feature matrix of 96X800 for training image and 96X1600 feature matrix for testing samples.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 3. Applying Wiener Filter on noisy image
<p>The Wiener filter is the mean square error-optimal stationary linear filter for images<br> degraded by additive noise and blurring. It removes the additive noise and inverts the blurring<br> simultaneously. The Wiener filtering is a linear estimation of the original image. The approach is<br> based on a stochastic framework.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 2. Original image affected by Gaussian noise
<p>In this research work we have detected Gaussian noise (fig 2) pattern in our images based on<br> methodology discussed in [18]. Where statistical moments features are extracted from the noise<br> patterns for noise class detection. This experiment detects the Gaussian noise patterns from images.<br> This leads to applying of wiener filter on noisy images, which gives the best noise removal.</p>
Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>This paper mainly focuses on automated detection of White Matter Lesions of brain using<br> fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the<br> representation of an image into a meaningful image and makes it easier to analyze. As a first step,<br> MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient<br> image enhancement techniques. The pre-processed image is subjected to clustering. The clustering<br> algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC)<br> and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to<br> initialization and are easily trapped in local optima. In order to obtain an optimized result, the<br> clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic<br> global optimization tool which is used in many optimization problems. Figure 2 represents overall<br> process of automatic detection of WMLs of brain. Since MS lesions present different characteristics<br> from lesions in elderly individuals there are many clustering models to determine the accuracy but<br> those methods are not directly applicable to predict the accurate lesions because of the decreased<br> contrast between White Matter and Grey Matter in elderly people. The proposed clustering models<br> are derived by extending the objective functions of FCM and Possibilistic clustering with a<br> Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and<br> is shown to be more robust to noise and other artifacts than competing approaches.</p>
Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<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.</p>
Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<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. The optimized result of GFMC<br> provides accurate detection of WMLs and it properly detects 195 images.</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.