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 Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Brain Tumor Classification using Deep Learning Algorithms

2021 ◽  
Vol 9 (12) ◽  
pp. 417-426
Author(s):  
Ankita Kadam
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Classification ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)

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.

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 Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors

2017 ◽  
Vol 37 (11) ◽  
pp. 3475-3487 ◽  
Author(s):  
Rebecca W Pak ◽  
Darian H Hadjiabadi ◽  
Janaka Senarathna ◽  
Shruti Agarwal ◽  
Nitish V Thakor ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Clinical Studies ◽  
Neurovascular Unit ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Preclinical And Clinical Studies

Functional magnetic resonance imaging (fMRI) serves as a critical tool for presurgical mapping of eloquent cortex and changes in neurological function in patients diagnosed with brain tumors. However, the blood-oxygen-level-dependent (BOLD) contrast mechanism underlying fMRI assumes that neurovascular coupling remains intact during brain tumor progression, and that measured changes in cerebral blood flow (CBF) are correlated with neuronal function. Recent preclinical and clinical studies have demonstrated that even low-grade brain tumors can exhibit neurovascular uncoupling (NVU), which can confound interpretation of fMRI data. Therefore, to avoid neurosurgical complications, it is crucial to understand the biophysical basis of NVU and its impact on fMRI. Here we review the physiology of the neurovascular unit, how it is remodeled, and functionally altered by brain cancer cells. We first discuss the latest findings about the components of the neurovascular unit. Next, we synthesize results from preclinical and clinical studies to illustrate how brain tumor induced NVU affects fMRI data interpretation. We examine advances in functional imaging methods that permit the clinical evaluation of brain tumors with NVU. Finally, we discuss how the suppression of anomalous tumor blood vessel formation with antiangiogenic therapies can “normalize” the brain tumor vasculature, and potentially restore neurovascular coupling.

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 Detection by Fusing Machine Learning and Neural Network Practices

2019 ◽  
Vol 8 (12S) ◽  
pp. 108-111
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Cell Number ◽  
Resonance Imaging ◽  
Artificial Neural Network Ann ◽  
The Brain ◽  
External Development ◽  
Living Being

An unusual cell number or mass in a living being brain is termed as “brain tumor”. A living being’s brain is present in the skull and the skull is very stiff in nature. Any external development within such a rigid space can trigger serious difficulties in the living being body. Tumors in the brain of a living being may be cancerous or may not. Therefore, the main cure is the detections of the brain tumor, its magnitude, and place. This study paper proposes a combination of approaches which integrates statistical methods and machine-based training practices “Support for the Vector Machine (SVM)” and the “Artificial Neural Network (ANN)” to achieve greater efficiency in brain tumors and in their phase’s identification as well as their place within magnetic resonance imaging pictures. In order to divide the magnetic resonance imaging pictures, an enhanced variant of standard “K-means” with Fuzzy C-means and temperature-based K-means & altered fuzzy clustering means. The value of K in the suggested method is an enhanced value, therefore, assists the fuzzy c to mean technique to perceive the tumor area

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