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 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 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.

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.

U mỡ trong xương bướm: một trường hợp hiếm gặp

2021 ◽  
Author(s):  
Thu Hien Trinh Thi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rare Case ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Sphenoid Bone ◽  
Resonance Imaging ◽  
Large Tumor ◽  
Intraosseous Lipoma

TÓM TẮT U mỡ trong xương là khối u lành tính hiếm gặp, thường gặp ở các xương dẹt, hiếm gặp ở xương nền sọ, đặc biệt là xương bướm. Trong đa số các trường hợp, u mỡ trong xương bướm thường được phát hiện tình cờ qua chụp cắt lớp vi tinh (CLVT) hoặc cộng hưởng từ (CHT) sọ não. Đây là một khối u phát triển chậm, ít gây ra triệu chứng, một số trường hợp gây triệu chứng khi khối u to chèn ép vào cấu trúc lân cận như tuyến yên hoặc dây thần kinh thị. Trong bài này, chúng tôi báo cáo một trường hợp u mỡ trong xương bướm không triệu chứng được phát hiện tình cờ và được chẩn đoán dựa vào phim chụp cộng hưởng từ sọ não. Bệnh nhân được khuyến nghị theo dõi định kỳ bằng cộng hưởng từ mà không phải tiến hành bất kỳ phương pháp điều trị nào. Từ khóa: U mỡ, xương bướm, MRI, cộng hưởng từ sọ não, chẩn đoán hình ảnh. ABSTRACT INTRAOSSEOUS LIPOMA OF SPHENOID BONE: A RARE CASE REVIEW Intraosseous lipoma is very rare, usually benign tumor of flat bones. The incidence of an intraosseous lipomalocated basal skull bones is extremely rare, especially in sphenoid bone. Radiological imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) are used to detect the intraosseous lipoma by accident. These tumors are slow growing and usually asymptomatic, in some cases causing symptoms when the large tumor presses on nearby structures such as pituitary gland or the optic nerve. We present a rare case of lipomaof the sphenoid bone discovered incidentally with brain magnetic resonance imaging. The patient has been followed-up by magnetic resonance imaging without the need for surgery. Keywords: Intraosseous lipoma, sphenoid bone, MRI, brain MRI, diagnostic radiology

scholarly journals Perfusion Magnetic Resonance Imaging to Assess Brain Tumor Responses to New Therapies

1998 ◽  
Vol 5 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Michael H. Lev ◽  
Fred Hochberg
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Normal Brain ◽  
Resonance Imaging ◽  
New Therapies ◽  
Magnetic Resonance Imaging Mri

Background: Although magnetic resonance imaging (MRI) is effective in detecting the location of intracranial tumors, new imaging techniques have been studied that may enhance the specificity for the prediction of histologic grade of tumor and for the distinction between recurrence and tumor necrosis associated with cancer therapy. Methods: The authors review their experience and that of others on the use of perfusion magnetic resonance imaging to evaluate responses of brain tumors to new therapies. Results: Functional imaging techniques that can distinguish tumor from normal brain tissue using physiological parameters. These new approaches provide maps of tumor perfusion to monitor the effects of novel compounds that restrict tumor angiogenesis. Conclusions: Perfusion MRI not only may be as effective as radionuclide-based techniques in sensitivity and specificity in assessing brain tumor responses to new therapies, but also may offer higher resolution and convenient co-registration with conventional MRI, as well as time- and cost-effectiveness. Further study is needed to determine the role of perfusion MRI in assessing brain tumor responses to new therapies.

scholarly journals Identifying Type 2 Diabetic Brains by Investigating Disease-Related Structural Changes in Magnetic Resonance Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuna Chen ◽  
Yongsheng Pan ◽  
Shangyu Kang ◽  
Junshen Lu ◽  
Xin Tan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Structure ◽  
Brain Mri ◽  
Brain Diseases ◽  
Imaging Biomarkers ◽  
Resonance Imaging ◽  
High Blood Glucose ◽  
The Brain

Diabetes with high blood glucose levels may damage the brain nerves and thus increase the risk of dementia. Previous studies have shown that dementia can be reflected in altered brain structure, facilitating computer-aided diagnosis of brain diseases based on structural magnetic resonance imaging (MRI). However, type 2 diabetes mellitus (T2DM)-mediated changes in the brain structures have not yet been studied, and only a few studies have focused on the use of brain MRI for automated diagnosis of T2DM. Hence, identifying MRI biomarkers is essential to evaluate the association between changes in brain structure and T2DM as well as cognitive impairment (CI). The present study aims to investigate four methods to extract features from MRI, characterize imaging biomarkers, as well as identify subjects with T2DM and CI.

Safety and Feasibility of Magnetic Resonance Imaging of the Brain at 1.5 Tesla in Patients with Temporary Transmyocardial Pacing Leads

2017 ◽  
Vol 67 (02) ◽  
pp. 086-091 ◽  
Author(s):  
Rami Homsi ◽  
Fritz Mellert ◽  
Roger Luechinger ◽  
Daniel Thomas ◽  
Jonas Doerner ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Troponin I ◽  
Brain Mri ◽  
Maximum Temperature ◽  
Safe Procedure ◽  
Resonance Imaging ◽  
The Brain ◽  
Head Coil ◽  
Pacing Leads

Background Temporary transmyocardial pacing leads (TTPLs) represent an absolute contraindication to magnetic resonance imaging (MRI). The purpose of this study was to evaluate the safety and feasibility of MRI at 1.5 Tesla (T) using a transmit/receive (T/R) head coil in patients with TTPL. Methods TTPLs (220 cm, Osypka TME, Dr. Osypka GmbH, Rheinfelden, Germany) were implanted in a phantom and exposed to conditions of a 1.5 T brain examination using a T/R head coil. Temperature changes at the lead tip were continuously recorded. A total of 28 patients with TTPL and an urgent indication for a brain MRI underwent MRI at 1.5 T with vital sign monitoring. A T/R head coil was used to minimize radiofrequency exposure of the TTPL. Before and immediately after the MRI scan, TTPL lead impedance, pacing capture threshold (PCT), signal slope, and sensing were measured. Serum troponin I was determined before and after MRI to detect thermal myocardial injury. Results In vitro, the maximum temperature increase from radiofrequency-induced heating of the TTPL tip was < 1°C. In vivo, no complications, such as heating sensations, dizziness, unexpected changes in heart rate or rhythm, or other unusual signs or symptoms were observed. No significant changes in the lead impedance, PCT, signal slope, or sensing were recorded. There were no increases of serum troponin I after the MRI examination. Conclusions MRI of the brain may be performed safely at 1.5 T using a T/R head coil in case of an urgent clinical need in patients with TTPL and may be considered a feasible and safe procedure when appropriate precautionary measures are taken.

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