The Potential of Using Brain Images for Authentication

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

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Impact on Brain due to Alcoholism using Improved Fuzzy C-Regression based Alcohol Detection

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a5309.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1317-1325

Keyword(s):

Risk Factors ◽

Human Brain ◽

Gray Matter ◽

Membership Functions ◽

Brain Images ◽

Men And Women ◽

Brain Image ◽

Left Thalamus ◽

Posterior Insula ◽

The Brain

In general, two risk factors such as alcohol expectancy and impulsivity have been concerned with alcohol abuse Currently, many people have been addicted to alcoholism and have an Alcohol Use Disorder (AUD) that affects neurons behavior in the human brain. Still, how such risk factors interrelate to estimate the alcoholism. To solve this problem, Fuzzy C-Regression based Alcoholism Detection (FCRAD) method has been proposed that segments the Region-Of-Interests (ROIs) from the human brain image to predict the Gray Matter Volume (GMV) reduction in the right posterior insula in women and the left thalamus in both men and women efficiently. However, it requires the detection of GMV reduction in the other brain image regions. This multi-modality can decrease the fuzziness of the partition and the crisp membership degrees were not derived easily. Therefore in this article, the GMV reduction in other regions of the brain images including right posterior insula in women and left thalamus in both men and women has been detected, an Improved FCRAD (IFCRAD) method is proposed to simplify the segmentation of the brain images by considering the second regularization term in the objective function of the FCR to take into account the noisy data. Also, the Euclidean distance is replaced with the Voronoi distance for computing different fuzzy membership functions. Moreover, new error measure and reward function are used in the objective function of the FCR to reward nearly crisp membership functions and to obtain more crisp partition. So, the brain images are segmented into gray-matter images that derive the ROIs to analyze the GMV reduction with less complexity. Finally, the experimental results illustrate the proposed IFCRAD method achieves higher accuracy than the existing AD methods.

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T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance

10.1101/171892 ◽

2017 ◽

Author(s):

John D Lewis ◽

Alan C Evans ◽

Jussi Tohka

Keyword(s):

Brain Development ◽

Cognitive Performance ◽

Gray Matter ◽

Brain Regions ◽

Brain Maturation ◽

Chronological Age ◽

Brain Images ◽

Cortical Gray Matter ◽

The Brain ◽

Age Prediction

The maturational schedule of human brain development appears to be narrowly confined. The chronological age of an individual can be predicted from brain images with considerable accuracy, and deviation from the typical pattern of brain maturation has been related to cognitive performance. Methods using multi-modal data, or complex measures derived from voxels throughout the brain have shown the greatest accuracy, but are difficult to interpret in terms of the biology. Measures based on the cortical surface(s) have yielded less accurate predictions, suggesting that perhaps developmental changes related to cortical gray matter are not strongly related to chronological age, and that perhaps development is more strongly related to changes in subcortical regions or in deep white matter. We show that a simple metric based on the white/gray contrast at the inner border of the cortical gray-matter is a comparably good predictor of chronological age, and our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are more strongly related to IQ than are those from cortical thickness, suggesting that this metric is more sensitive to aspects of brain development that reflect cognitive performance.

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Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

International Journal of Biomedical Imaging ◽

10.1155/2011/350838 ◽

2011 ◽

Vol 2011 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Vincent Michel ◽

Evelyn Eger ◽

Christine Keribin ◽

Bertrand Thirion

Keyword(s):

Brain Regions ◽

Bayesian Regression ◽

Data Sets ◽

Brain Images ◽

Popular Approach ◽

Prediction Function ◽

Neuroimaging Data ◽

Behavioral Parameters ◽

The Brain ◽

Better Than

Inverse inferencehas recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, calledMulticlass Sparse Bayesian Regression(MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features.

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Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain

Nuklearmedizin ◽

10.1055/s-0038-1625192 ◽

2003 ◽

Vol 42 (05) ◽

pp. 215-219

Author(s):

G. Platsch ◽

A. Schwarz ◽

K. Schmiedehausen ◽

B. Tomandl ◽

W. Huk ◽

...

Keyword(s):

Data Sets ◽

3D Mri ◽

Clinical Routine ◽

Fusion Procedure ◽

3D Data ◽

Mri Scans ◽

2D Data ◽

Time Required ◽

The Brain ◽

Manual Correction

Summary: Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and Method: In 32 patients regional cerebral blood flow was measured using 99mTc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

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Faculty Opinions recommendation of Accelerated brain gray matter loss in fibromyalgia patients: premature aging of the brain?

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1090657.543957 ◽

2007 ◽

Author(s):

Irwin Jon Russell

Keyword(s):

Gray Matter ◽

Premature Aging ◽

Gray Matter Loss ◽

The Brain

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Voxel-based correlation of 18F-THK5351 accumulation and gray matter volume in the brain of cognitively normal older adults

EJNMMI Research ◽

10.1186/s13550-019-0552-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Yoko Shigemoto ◽

Daichi Sone ◽

Miho Ota ◽

Norihide Maikusa ◽

Masayo Ogawa ◽

...

Keyword(s):

Older Adults ◽

Gray Matter ◽

Gray Matter Volume ◽

Cognitively Normal ◽

Matter Volume ◽

The Brain

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Design For An Automated Recognition System For Multilayer Circuit Board Testing

10.1117/12.959560 ◽

1980 ◽

Author(s):

Charles C. Cheng

Keyword(s):

Recognition System ◽

Circuit Board ◽

Automated Recognition ◽

Circuit Board Testing

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Nervous Activity of the Brain in Five Dimensions

Biophysica ◽

10.3390/biophysica1010004 ◽

2021 ◽

Vol 1 (1) ◽

pp. 38-47

Author(s):

Arturo Tozzi ◽

James F. Peters ◽

Norbert Jausovec ◽

Arjuna P. H. Don ◽

Sheela Ramanna ◽

...

Keyword(s):

Fourier Analysis ◽

Nervous Activity ◽

Three Dimensional ◽

Three Dimensions ◽

Data Sets ◽

Spatial And Temporal Patterns ◽

Five Dimensions ◽

Four Dimensions ◽

Scale Free ◽

The Brain

The nervous activity of the brain takes place in higher-dimensional functional spaces. It has been proposed that the brain might be equipped with phase spaces characterized by four spatial dimensions plus time, instead of the classical three plus time. This suggests that global visualization methods for exploiting four-dimensional maps of three-dimensional experimental data sets might be used in neuroscience. We asked whether it is feasible to describe the four-dimensional trajectories (plus time) of two-dimensional (plus time) electroencephalographic traces (EEG). We made use of quaternion orthographic projections to map to the surface of four-dimensional hyperspheres EEG signal patches treated with Fourier analysis. Once achieved the proper quaternion maps, we show that this multi-dimensional procedure brings undoubted benefits. The treatment of EEG traces with Fourier analysis allows the investigation the scale-free activity of the brain in terms of trajectories on hyperspheres and quaternionic networks. Repetitive spatial and temporal patterns undetectable in three dimensions (plus time) are easily enlightened in four dimensions (plus time). Further, a quaternionic approach makes it feasible to identify spatially far apart and temporally distant periodic trajectories with the same features, such as, e.g., the same oscillatory frequency or amplitude. This leads to an incisive operational assessment of global or broken symmetries, domains of attraction inside three-dimensional projections and matching descriptions between the apparently random paths hidden in the very structure of nervous fractal signals.

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