Modern standards for magnetic resonance imaging of the brain tumors

2020 ◽  
Vol 84 (3) ◽  
pp. 102
Author(s):  
N.E. Zakharova ◽  
I.N. Pronin ◽  
A.I. Batalov ◽  
E.I. Shults ◽  
A.N. Tyurina ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Resonance Imaging ◽  
The Brain

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 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 An Employment of Probabilistic Neural Network in Magnetic Resonance Imaging For Early Brain Cancer Detection

2021 ◽  
Vol 12 (2) ◽  
pp. 2584-2592
Author(s):  
Seba Aziz Sahym
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Cancer Detection ◽  
Brain Cancer ◽  
Probabilistic Neural Network ◽  
Common Disease ◽  
Resonance Imaging ◽  
The Brain

Given the circumstances of the countries in which wars, political instability, and other uncertainties are passing that make the atmosphere impure, which have caused many diseases, one of these diseases that has spread widely is cancer. Cancer is a very common disease, and many of them affect a person and lead him or her to death. Among these diseases, which have been common in recent years specifically the brain tumors that they need early diagnosis and do not cause the death of the person. Furthermore, many studies in the field of brain cancer detection have been done, but the best solution is still missing. Therefore, in this paper, a reliable method is proposed to detect brain tumors, extract its properties, and classify the tumor using Magnetic Resonance Imaging (MRI) through the artificial neural network.  In the proposed system, an essential part of image processing is the analysis and processing of digital images, especially to improve their quality, Bilateral Filter is used to improving image clarity and any image noise in this method preserves edges. After that, the distinctive properties of the image are extracted using the Histogram of Oriented Gradient (HOG) method. Thus, the extracted features are strong and can be classified as a Probabilistic Neural Network (PNN), this is what distinguishes our work from the previous works. The advantage obtained is granted to the PNN Classifier, which is used to train and test the accuracy of performance in perceiving the location of the tumour in MRI images of the brain accuracy as it resolves 99.5%.

scholarly journals Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 372
Author(s):  
Muhannad Faleh Alanazi ◽  
Muhammad Umair Ali ◽  
Shaik Javeed Hussain ◽  
Amad Zafar ◽  
Mohammed Mohatram ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Brain Mri ◽  
Learning Model ◽  
High Accuracy ◽  
Resonance Imaging ◽  
Deep Learning Model ◽  
The Brain

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

Сauses of Dizziness in Patients with Suspected Stroke

2018 ◽  
Vol 7 (3) ◽  
pp. 217-221
Author(s):  
E. V. Shevchenko ◽  
G. R. Ramazanov ◽  
S. S. Petrikov
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Demyelinating Disease ◽  
Heart Rhythm ◽  
Diagnostic Methods ◽  
Deficiency Anemia ◽  
Resonance Imaging ◽  
Peripheral Vestibulopathy ◽  
Suspected Stroke ◽  
The Brain

Background Acute dizziness may be the only symptom of stroke. Prevalence of this disease among patients with isolated dizziness differs significantly and depends on study design, inclusion criteria and diagnostic methods. In available investigations, we did not find any prospective studies where magnetic resonance imaging, positional maneuvers, and Halmagyi-Curthoys test had been used to clarify a pattern of diseases with isolated acute dizziness and suspected stroke.Aim of study To clarify the pattern of the causes of dizziness in patients with suspected acute stroke.Material and methods We examined 160 patients admitted to N.V. Sklifosovsky Research Institute for Emergency Medicine with suspected stroke and single or underlying complaint of dizziness. All patients were examined with assessment of neurological status, Dix-Hollpike and Pagnini-McClure maneuvers, HalmagyiCurthoys test, triplex scans of brachiocephalic arteries, transthoracic echocardiography, computed tomography (CT) and magnetic resonance imaging (MRI) of the brain with magnetic field strength 1.5 T. MRI of the brain was performed in patients without evidence of stroke by CT and in patients with stroke of undetermined etiology according to the TOAST classification.Results In 16 patients (10%), the cause of dizziness was a disease of the brain: ischemic stroke (n=14 (88%)), hemorrhage (n=1 (6%)), transient ischemic attack (TIA) of posterior circulation (n=1 (6%)). In 70.6% patients (n=113), the dizziness was associated with peripheral vestibulopathy: benign paroxysmal positional vertigo (n=85 (75%)), vestibular neuritis (n=19 (17%)), Meniere’s disease (n=7 (6%)), labyrinthitis (n=2 (1,3%)). In 6.9% patients (n=11), the cause of dizziness was hypertensive encephalopathy, 1.9% of patients (n=3) had heart rhythm disturbance, 9.4% of patients (n=15) had psychogenic dizziness, 0.6% of patients (n=1) had demyelinating disease, and 0.6% of patients (n=1) had hemic hypoxia associated with iron deficiency anemia.Conclusion In 70.6% patients with acute dizziness, admitted to hospital with a suspected stroke, peripheral vestibulopathy was revealed. Only 10% of patients had a stroke as a cause of dizziness.

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