scholarly journals Preprocessing and Skull Stripping of Brain Tumor Extraction from Magnetic Resonance Imaging Images Using Image Processing

2021 ◽  
Author(s):  
Shweta Suryawanshi ◽  
Sanjay B. Patil
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Regions ◽  
Resonance Imaging ◽  
Skull Stripping ◽  
Intensity Changes ◽  
Tumor Extraction ◽  
Physical Reasons ◽  
The Brain

Many neuroimaging processing functions believe the preprocessing and skull strip (SS) to be an important step in brain tumor diagnosis. For complex physical reasons intensity changes in brain structure and magnetic resonance imaging of the brain, a proper preprocessing and SS is an important part. The method of removing the skull is relayed to the taking away of the skull area in the brain for medical investigation. It is more correct and necessary techniques for distinguishing between brain regions and cranial regions and this is believed a demanding task. This paper gives detailed review on the preprocessing and traditional transition to machine learning and deep learning-based automatic SS techniques of magnetic resonance imaging.

Functional Connectivity Magnetic Resonance Imaging Reveals Rapid and Reversible Changes in the Brain Following Induction of Psoriasiform Dermatitis in Mice

2018 ◽  
Vol 3 (2) ◽  
pp. 59-64
Author(s):  
Xiping Liu ◽  
Yasutomo Imai ◽  
Yan Zhou ◽  
Sebastian Yu ◽  
Rupeng Li ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Skin Inflammation ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Mouse Ear ◽  
The Brain

Functional connectivity magnetic resonance imaging (fcMRI), a specific form of MRI imaging, quantitatively assesses connectivity between brain regions that share functional properties. Functional connectivity magnetic resonance imaging has already provided unique insights into changes in the brain in patients with conditions such as depression and pain and symptoms that have been reported by patients with psoriasis and are known to impact quality of life. To identify the central neurological impact of psoriasiform inflammation of the skin, we applied fcMRI analysis to mice that had been topically treated with the Toll-like receptor agonist, imiquimod (IMQ) to induce psoriasiform dermatitis. Brain insula regions, due to their suggested role in stress, were chosen as seed regions for fcMRI analysis. Mouse ear and head skin developed psoriasiform epidermal thickening (up to 4-fold, P < .05) and dermal inflammation after 4 days of topical treatment with IMQ. After fcMRI analysis, IMQ-treated mice showed significantly increased insula fc with wide areas throughout the brain, including, but not limited to, the somatosensory cortex, anterior cingulate cortex, and caudate putamen ( P < .005). This reflects a potential central neurological impact of IMQ-induced psoriasis-like skin inflammation. These data indicate that fcMRI may be valuable tool to quantitatively assess the neurological impact of skin inflammation in patients with psoriasis.

scholarly journals Edge Detection to Indication Brain Tumor Using Sobel and Morphological Operations Methods Based on Image Magnetic Resonance Imaging

2018 ◽  
Vol 3 (2) ◽  
pp. 179
Author(s):  
Oscar Adriyanto ◽  
Halim Agung
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Edge Detection ◽  
Morphological Operations ◽  
Resonance Imaging ◽  
Edge Detection Method ◽  
Tumor Edge ◽  
The Brain

Brain tumors are the second leading cause of death in the world in children under 20, scientists and researchers are developing applications to react brain tumors based on magnetic resonance imaging images. In this application the method used is sobel and morphological operations. Based on research conducted on brain tumor edge detection based on magnetic resonance imaging image, sobel method can reduce the noise contained in the image mri and can localize the edge of the image of Magnetic Resonance Imaging well. This research can conclude that the sobel method is suitable for edge detection but there is still some unprocessed noise, with the results of the brain imaging of 30 test images have 60% percentage, while for the use of edge detection method of 62.11%.

scholarly journals Postictal Magnetic Resonance Imaging Changes Masquerading as Brain Tumor Progression: A Case Series

2016 ◽  
Vol 9 (2) ◽  
pp. 358-362 ◽  
Author(s):  
Anastasie M. Dunn-Pirio ◽  
Santoshi Billakota ◽  
Katherine B. Peters
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Progression ◽  
Relevant Literature ◽  
Case Series ◽  
Resonance Imaging ◽  
The Brain ◽  
Radiographic Changes

Seizures are common among patients with brain tumors. Transient, postictal magnetic resonance imaging abnormalities are a long recognized phenomenon. However, these radiographic changes are not as well studied in the brain tumor population. Moreover, reversible neuroimaging abnormalities following seizure activity may be misinterpreted for tumor progression and could consequently result in unnecessary tumor-directed treatment. Here, we describe two cases of patients with brain tumors who developed peri-ictal pseudoprogression and review the relevant literature.

scholarly journals Distribution of multiple sclerosis lesions detected by brain magnetic resonance imaging in Erbil city

2021 ◽  
Vol 25 (1) ◽  
pp. 446-455
Author(s):  
Dina Tawfeeq ◽  
Shawnam Dawood
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Corpus Callosum ◽  
Brain Stem ◽  
Signal Intensity ◽  
Brain Regions ◽  
Multiple Sclerosis Lesion ◽  
Resonance Imaging ◽  
The Brain

Background and objective: Many epidemiological studies and clinical manifestation studies of multiple sclerosis have been done in Iraq. Up to our knowledge, no such observational study to the radiological feature of the multiple sclerosis lesion has been done yet in Erbil in comparison to other worldwide studies. This study aimed to assess the distribution of multiple sclerosis lesions in brain regions detected by magnetic resonance imaging among Erbil population. Methods: This was a cross-sectional study conducted at the College of Medicine, Hawler Medical University, from April 2018 to July 2019. A review of magnetic resonance imaging scans of the brain of 120 patients was done. Special attention was directed toward identifying the variance in multiple sclerosis lesions distribution in the brain regions and their MR signal intensity characteristics. Results: Periventricular lesions were observed in more than 90% of the study sample. The next common was juxtacortical lesions (24.8%), followed by corpus callosum lesions (16.8 %), while brain stem lesions were the least observed proportions. No significant difference was detected in the distribution of multiple sclerosis lesions among ethnicities and genders, except for basal ganglia lesions, which were significantly more common in women (P = 0.016).The magnetic resonance imaging signal intensity of the lesion was significantly variable among disease duration. Conclusion: The T2 hyper intense lesions were most commonly seen in the periventricular region. Juxtacortical and corpus callosum lesions were also frequently observed. The proportions of the brain stem and cerebellum lesions appeared to be lower in comparison to previous studies. Keywords: Multiple Sclerosis; Magnetic Resonance Imaging; Distribution; Lesion.

scholarly journals Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

2020 ◽  
Vol 16 (12) ◽  
pp. 116
Author(s):  
Hamed Samadi Ghoushchi ◽  
Yaghoub Pourasad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Support Vector ◽  
Statistical Features ◽  
Resonance Imaging ◽  
The Brain

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

scholarly journals Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

scholarly journals The Impact of Undiagnosed Synaesthesia on the Interpretation of Structural and Functional MRI Images Connectivity Maps and Resulting Diagnoses

2016 ◽  
Vol 15 (11) ◽  
pp. 7227-7234
Author(s):  
Nourhan Zayed
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Sensory Modality ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Resonance Imaging ◽  
Functional Connections ◽  
The Impact ◽  
The Brain

Synathesia is a condition in which stimulation of a sensory modality triggers another sensation in the alike or an unalike sensory modality. Currently, synaesthesia is deemed a neurological condition that engages unwanted transfer of signals between brain regions from one sense to another “crosstalk activation”. The probability that undiagnosed synaesthesia may impact the results of structural magnetic resonance imaging (MRI), Diffusion Tensor imaging (DTI), functional magnetic resonance imaging (fMRI) and resting state connectivity studies is high, given the multiple anatomical and functional connections within the brain. In this paper, the currently available literature to mark which sensations adjured by synaesthesia and how could this impact MRI different modalities. Our study found that synaesthesia can have an opaque impact on fMRI studies of sensory, memory and cognitive functions, and there is testimony to suggest structural connections in the brain are also mutated DTI measurements especially, it shows enhanced structural connectivity for synesthetes between brain regions, higher Fractional anisotropy (FA), as well as increased in the white matter integrity between some regions.. Given the low dispersal of synaesthesia, the likelihood of synaesthesia being a perplexing factor in DTI, fMRI studies of patient groups is small; however, determining the existence of synaesthesia is paramount for investigating individual patients especially Shizoherenia, and autistic patients.

scholarly journals Regional brain activation/deactivation during word generation in schizophrenia: fMRI study

2011 ◽  
Vol 198 (3) ◽  
pp. 213-222 ◽  
Author(s):  
John P. John ◽  
Harsha N. Halahalli ◽  
Mandapati K. Vasudev ◽  
Peruvumba N. Jayakumar ◽  
Sanjeev Jain
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Regions ◽  
Repetition Condition ◽  
Healthy Individuals ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Word Generation ◽  
The Brain

BackgroundExamination of the brain regions that show aberrant activations and/or deactivations during semantic word generation could pave the way for a better understanding of the neurobiology of cognitive dysfunction in schizophrenia.AimsTo examine the pattern of functional magnetic resonance imaging blood oxygen level dependent activations and deactivations during semantic word generation in schizophrenia.MethodFunctional magnetic resonance imaging was performed on 24 participants with schizophrenia and 24 matched healthy controls during an overt, paced, ‘semantic category word generation’ condition and a baseline ‘word repetition’ condition that modelled all the lead-in/associated processes involved in the performance of the generation task.ResultsThe brain regions activated during word generation in healthy individuals were replicated with minimal redundancies in participants with schizophrenia. The individuals with schizophrenia showed additional activations of temporo-parieto-occipital cortical regions as well as subcortical regions, despite significantly poorer behavioural performance than the healthy participants. Importantly, the extensive deactivations in other brain regions during word generation in healthy individuals could not be replicated in those with schizophrenia.ConclusionsMore widespread activations and deficient deactivations in the poorly performing participants with schizophrenia may reflect an inability to inhibit competing cognitive processes, which in turn could constitute the core information-processing deficit underlying impaired word generation in schizophrenia.

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