Visualization of Hidden Structure and Shape in Ct Image via Non-Linear Perspective Foreground and Back Ground Projection

2021 ◽  
Author(s):  
parthee pan ◽  
Raja Paul Perinbam ◽  
Krishna Murthy ◽  
Shanker Rajendiran Nagalingam ◽  
krishna kumari s ◽  
...  
Keyword(s):  
Occipital Lobe ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
Percent Accuracy ◽  
The Brain ◽  
Ct And Mri

Abstract The neurologist analyse the brain images to diagnose the disease via structure and shape of the part in the scanned Medical images such as CT, MRI, and PET.The Medical image segmentation perform less in the regions where no or little contrast,artefacts over the different boundary regions. The manual process of segmentation show poor boundary differentiation dueto discernibility in shape and location, intra and inter observer reliability. In this paper, we propose a dyadic Cat optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non- linear perspective Foreground and Back Ground projection. The DCO algorithm remove the artefacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm show the region boundary such as plerygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture with high visibility in the regions of inadequately visible boundary and distinguish the deformable shape. The DCO algorithm show the increased SSIM and 90 percent accuracy.

Visualization of Pterygomaxillary Fissure Structure and Shape in CT Image via Non-Linear Perspective Projection

2021 ◽  
Vol 11 (6) ◽  
pp. 1580-1589
Author(s):  
R. Partheepan ◽  
J. Raja Paul Perinbam ◽  
M. Krishnamurthy ◽  
N. R. Shanker
Keyword(s):  
Similarity Index ◽  
Occipital Lobe ◽  
Structural Similarity ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
The Brain

The neurologist analyses the brain images to diagnose disease via structure and shape of the part in scanned Medical images such as CT, MRI, and PET. The Medical image segmentation performs less in the regions where no or little contrast, artifacts over the different boundary regions. The manual process of segmentation shows poor boundary differentiation due to discernibility in shape and location, intra and inter observer reliability. In this paper, we propose dyadic CAT optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non-linear perspective Foreground and Back Ground projection. The DCO algorithm removes the artifacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm shows the region boundary for pterygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture in brain image with high visibility in the regions of inadequately visible boundary and distinguishes the deformable shape. The DCO algorithm applies on 50 images and eight images with complex bone and muscle mass structure for performance evaluation. The DCO algorithm shows the increased Structural similarity index (SSIM) with 90% accuracy.

First and Second Language Patterns of Brain Activation in Korean Layrngeal Contrasts

2008 ◽  
Vol 14 ◽  
pp. 1-19 ◽  
Author(s):  
Haeil Park ◽  
Gregory Iverson
Keyword(s):  
Second Language ◽  
Native Speakers ◽  
Occipital Lobe ◽  
Brain Regions ◽  
Second Language Speakers ◽  
Significant Difference ◽  
Native Speakers Of English ◽  
Specific Activities ◽  
The Right ◽  
The Brain

Abstract. This study aims to localize the brain regions involved in the apprehension of Korean laryngeal contrasts and to investigate whether the Internal Model advanced by Callan et al. (2004) extends to first versus second language perception of these unique three-way laryngeal distinctions. The results show that there is a significant difference in activation between native and second-language speakers, consistent with the findings of Callan et al. Specific activities unique to younger native speakers of Korean relative to native speakers of English were seen in the cuneus (occipital lobe) and the right middle frontal gyrus (Brodmann Area [BA] 10), areas of the brain associated with pitch perception. The current findings uphold Silva's (2006) conclusion that the laryngeal contrasts of Korean are increasingly distinguished less by VOT differences than by their effect on pitch in the following vowel. A subsequent experiment was conducted to establish whether more traditional, older native speakers of Korean who still make clear VOT distinctions also activate both the cuneus and BA 10 in the same task. Preliminary results indicate that they do not, whereas speakers with overlapping VOT distinctions do show intersecting activations in these areas, thus corroborating Silva's claim of emergent pitch sensitivity in the Korean laryngeal system.

Biochemical changes in the brain of hemiplegic migraine patients measured with 7 tesla 1H-MRS

Cephalalgia ◽  
2014 ◽  
Vol 34 (12) ◽  
pp. 959-967 ◽  
Author(s):  
R Zielman ◽  
WM Teeuwisse ◽  
F Bakels ◽  
J Van der Grond ◽  
A Webb ◽  
...  
Keyword(s):  
Occipital Lobe ◽  
Brain Regions ◽  
Biochemical Changes ◽  
High Spectral Resolution ◽  
7 Tesla ◽  
Healthy Controls ◽  
Hemiplegic Migraine ◽  
1H Mrs ◽  
Total N ◽  
The Brain

Aim The aim of this study was to assess biochemical changes in the brain of patients with hemiplegic migraine in between attacks. Methods Eighteen patients with hemiplegic migraine (M:F, 7:11; age 38 ± 14 years) of whom eight had a known familial hemiplegic migraine (FHM) mutation (five in the CACNA1A gene (FHM1), three in the ATP1A2 gene (FHM2)) and 19 age- and sex-matched healthy controls (M:F, 7:12; mean age 38 ±  12 years) were studied. We used single-voxel 7 tesla 1H-MRS (STEAM, TR/TM/TE = 2000/19/21 ms) to investigate four brain regions in between attacks: cerebellum, hypothalamus, occipital lobe, and pons. Results Patients with hemiplegic migraine showed a significantly lower total N-acetylaspartate/total creatine ratio (tNAA/tCre) in the cerebellum (median 0.73, range 0.59–1.03) than healthy controls (median 0.79, range (0.67–0.95); p = 0.02). In FHM1 patients with a CACNA1A mutation, the tNAA/tCre was lowest. Discussion We found a decreased cerebellar tNAA/tCre ratio that might serve as an early biomarker for neuronal dysfunction and/or loss. This is the first high-spectral resolution 7 tesla 1H-MRS study of interictal biochemical brain changes in hemiplegic migraine patients.

Gray matter blood flow change is unevenly distributed during moderate isocapnic hypoxia in humans

2008 ◽  
Vol 104 (1) ◽  
pp. 212-217 ◽  
Author(s):  
Andrew P. Binks ◽  
Vincent J. Cunningham ◽  
Lewis Adams ◽  
Robert B. Banzett
Keyword(s):  
Blood Flow ◽  
Nucleus Accumbens ◽  
Positron Emission Tomography Imaging ◽  
Occipital Lobe ◽  
Brain Regions ◽  
Functional Brain Imaging ◽  
Regions Of Interest ◽  
Breath Hold ◽  
Obstructive Sleep ◽  
The Brain

Hypoxia increases cerebral blood flow (CBF), but it is unknown whether this increase is uniform across all brain regions. We used H215O positron emission tomography imaging to measure absolute blood flow in 50 regions of interest across the human brain ( n = 5) during normoxia and moderate hypoxia. Pco2 was kept constant (∼44 Torr) throughout the study to avoid decreases in CBF associated with the hypocapnia that normally occurs with hypoxia. Breathing was controlled by mechanical ventilation. During hypoxia (inspired Po2 = 70 Torr), mean end-tidal Po2 fell to 45 ± 6.3 Torr (means ± SD). Mean global CBF increased from normoxic levels of 0.39 ± 0.13 to 0.45 ± 0.13 ml/g during hypoxia. Increases in regional CBF were not uniform and ranged from 9.9 ± 8.6% in the occipital lobe to 28.9 ± 10.3% in the nucleus accumbens. Regions of interest that were better perfused during normoxia generally showed a greater regional CBF response. Phylogenetically older regions of the brain tended to show larger vascular responses to hypoxia than evolutionary younger regions, e.g., the putamen, brain stem, thalamus, caudate nucleus, nucleus accumbens, and pallidum received greater than average increases in blood flow, while cortical regions generally received below average increases. The heterogeneous blood flow distribution during hypoxia may serve to protect regions of the brain with essential homeostatic roles. This may be relevant to conditions such as altitude, breath-hold diving, and obstructive sleep apnea, and may have implications for functional brain imaging studies that involve hypoxia.

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

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.

scholarly journals Medical Images Separation and Fusion Based on Artificial Neural Network

2014 ◽  
Vol 7 (3) ◽  
pp. 92-105
Author(s):  
Auns Q. H. Al-Neami ◽  
Cinan Kanaan A.R. Al Khuzaay
Keyword(s):  
Neural Network ◽  
Brain Regions ◽  
Fusion Process ◽  
Radiotherapy Planning ◽  
Fixed Point Algorithm ◽  
Image Patches ◽  
Oncology Treatment ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Ct And Mri

During the last few decades, the field of medical image processing has been closely related to neural network methodologies and their applications. In the present investigation a 512×512 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images for different region of the brain are registered to eliminate the dimensionality differences between the two images, then separated both of them by fast-fixed point algorithm after truncation of each image in to almost 1000 image patches of 15×15 dimension and transform them to 1-D and order them into row-wise fashion as well as reducing the entered data of lesser interest by Principle component analysis (PCA), finally applying the fusion process using different methods. The result shown that the differently defined brain regions can be separated using batch approaches for both CT and MRI and could be a powerful and accurate diagnostic tool, especially, for surgical and radiotherapy, planning and oncology treatment after a suitable fusion process is carried out on it.

Segmentation and clustering in brain MRI imaging

2018 ◽  
Vol 30 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Hojjat Adeli
Keyword(s):  
Grey Matter ◽  
State Of The Art ◽  
Brain Mri ◽  
Brain Regions ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Different Parts

AbstractClustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.

Alice in Wonderland Syndrome: An Update of Present Data With A Special View to Body Position, Traumatic and Genetic Aspects

2020 ◽  
Author(s):  
Stefan Bittmann
Keyword(s):  
Depressive Disorders ◽  
Parietal Lobe ◽  
Body Position ◽  
Occipital Lobe ◽  
Brain Regions ◽  
Lewis Carroll ◽  
Temporoparietal Junction ◽  
Alice In Wonderland ◽  
First Time ◽  
The Brain

Alice in Wonderland Syndrome (AIWS) was named after the description of Lewis Carroll in his novel. In 1955, John Todd, a psychiatrist described this entity for the first time and results in a distortion of perception. Todd described it as „Alice's Adventures in Wonderland“ by Lewis Carroll. The author Carroll suffered from severe migraine attacks. Alice in Wonderland Syndrome is a disorienting condition of seizures affecting visual perception. AIWS is a neurological form of seizures influencing the brain, thereby causing a disturbed perception. Patients describe visual, auditory, and tactile hallucinations and disturbed perceptions. The causes of AIWS are still not known exactly. Cases of migraine, brain tumors, depression episodes, epilepsy, delirium, psychoactive drugs, ischemic stroke, depressive disorders, and EBV, mycoplasma, and malaria infections are correlating with AIWS like seizures. Often no EEG correlate is found. Neuroimaging studies reveal disturbances of brain regions including the temporoparietal junction, the temporal and occipital lobe as typical localization of the visual pathway. A decrease of perfusion of the visual pathways could induce these disturbances, especially in the temporal lobe in patients with AIWS. Other theories suggest distorted body illusions stem from the parietal lobe. The concrete origin of this mysterious syndrome is to date not clearly defined.

scholarly journals Auditory Hallucinations as a Rare Presentation of Occipital Infarcts

2018 ◽  
Vol 2018 ◽  
pp. 1-4
Author(s):  
Firas Ido ◽  
Reina Badran ◽  
Brandon Dmytruk ◽  
Zain Kulairi
Keyword(s):  
Brain Mri ◽  
Occipital Lobe ◽  
Hemorrhagic Transformation ◽  
Acute Onset ◽  
Clinical Syndrome ◽  
Auditory Hallucinations ◽  
Rare Presentation ◽  
Brain Images ◽  
Delay In Diagnosis ◽  
The Brain

A stroke is a clinical syndrome characterized by a focal neurologic deficit that can be attributed to a vascular territory within the brain. The presenting features of an acute stroke depends on the area of the brain affected. Although unusual, the presenting feature may include psychosis with auditory and/or visual hallucinations. A 56-year-old female was admitted to the psychiatric unit after threatening her husband with a knife. She reported experiencing altered sensorium for one week with suicidal and homicidal command hallucinations. Given the acute onset, brain images were obtained to rule out an organic etiology. A brain MRI revealed an acute right occipital lobe infarct with hemorrhagic transformation. The patient’s symptoms were self-limited, resolving without antipsychotic medications. Psychosis with auditory hallucinations is not commonly reported following stroke. Since histologic and functional alterations in the occipital lobe appear to play a significant role in psychosis of schizophrenics, it is likely that ischemia in the same area may cause similar changes. Familiarity with this rare presentation is important, as it prevents a delay in diagnosis, which may negatively impact the outcome.

scholarly journals Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
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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