scholarly journals Brain Tumor Segmentation from Multimodal MRI Data based on GLCM and SVM Classifier

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Brain Tumor ◽  
Surgical Navigation ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Normal Brain ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Normal Tissues

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

scholarly journals Brain tumor segmentation from multimodal magnetic resonance imaging data based on gray-level co-occurrence matrix (GLCM) and an ensemble Support Vector Machine (SVM) classifier

2020 ◽  
Author(s):  
Na Li ◽  
Zheng Yang
Keyword(s):  
Brain Tumors ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Gray Level ◽  
Resonance Imaging ◽  
Dice Coefficient ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Occurrence Matrix

Abstract Background Brain tumors, abnormal cells growing in the human brain,are common neurological diseases that are extremely harmful to human health. Malignant brain tumors can lead to high mortality. Magnetic resonance imaging (MRI)༌a typical noninvasive imaging technology, can produce high-quality brain images without damage and skull artifacts, as well as provide comprehensive information to facilitate the diagnosis and treatment of brain tumors. Additionally༌the segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. Methods We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure. We focus on the effects of GLCM texture on brain tumor segmentation. First, 112 GLCM features for each voxel were extracted. Next, these features were ranked using the SVM-recursive feature elimination (SVM-RFE) method. Based on the sorting results, we found that when the number of features was 60, the value of the Dice similarity coefficient (DSC) tended to be flat. The GLCM texture features maximal correlation coefficient, information measure of correlation, Angular Second Moment, sum of squares, difference variance, contrast, and inverse difference moment were important for segmentation. Finally, we selected the top 60 grayscale features and constructed an ensemble SVM classifier to separate the abnormal mass of tissue from normal brain tissues. Results The experimental material was a dataset called BraTs2015. The proposed model was verified with the Dice coefficient. For low-grade tumors, we obtained a 91.2% average Dice coefficient for segmenting the complete tumor region. For high-grade tumors, the average was slightly higher at 92.4%. Conclusion Our results demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

scholarly journals Brain Tumor Segmentation using FCM and Symbolic Feature

2019 ◽  
Vol 8 (6S) ◽  
pp. 654-658
Keyword(s):  
Brain Tumor ◽  
Malignant Tumors ◽  
Median Filter ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Normal Tissues ◽  
Tumor Region ◽  
Mr Brain Images ◽  
The Brain

The brain tumor segmentation from image is interesting and challenging in the field of image processing and pattern recognition. An early detection of a brain tumor region helps the patient to take the correct medicine and increase the rate of the survival.The brain tumor segmentation is a process of differentiating the abnormal tissues and normal tissues. most common types of brain tumors are Benign and Malignant tumors. In this paper, the Fuzzy C-Means (FCM) approach is used to cluster the abnormal cells region and normal cells region in the brain image. The possible noises are removed by employing the median filter and morphological function is applied to extract the possible tumor region. The true tumor region is extracted with the help of symbolic features. Finally, the proposed methods is tested on T2- weighted MR brain images

scholarly journals Automated Brain Tumor Segmentation and Identification using MR Images

2019 ◽  
Vol 8 (12) ◽  
pp. 2647-2651
Keyword(s):  
Brain Tumor ◽  
Human Brain ◽  
Computational Time ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Automatic Identification ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Processing Step

Automatic identification of tumor in human brain is a challenging task due to its varying in size, shape and location. This paper proposes a multi-modality technique for the segmentation of brain tumor its classification to differentiate easily between cancerous and non-cancerous tumor from MR images of the human brain. To achieve this, different segmentation and classification techniques have been applied. The important stages involved in the proposed technique are pre-processing, segmentation and classification stages. The pre-processing step is carried out using wavelet transform, segmentation stage is done by applying modified Chan-Vese model and finally the extracted tumor can be classified as benign or malignant using Support Vector Machine (SVM) classifier. The experimental results on MR images prove that, the proposed method is efficient and robust to noise. Moreover, the comparisons with existing techniques also show that, the proposed method takes less computational time and classify the tumors very accuratel

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

scholarly journals Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
Yue Zhao ◽  
Xiaoqiang Ren ◽  
Kun Hou ◽  
Wentao Li
Keyword(s):  
Brain Tumor ◽  
Network Architecture ◽  
Contextual Information ◽  
Computational Cost ◽  
Disease Diagnosis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fiber Network ◽  
3D Network ◽  
The Brain

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.

scholarly journals Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 2051-2054
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Important Task ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Medical Issues ◽  
Current Scenario ◽  
Exact Position ◽  
The Brain

Medical image processing is an important task in current scenario as more and more humans are diagnosed with various medical issues. Brain tumor (BT) is one of the problems that is increasing at a rapid rate and its early detection is important in increasing the survival rate of humans. Detection of tumor from Magnetic Resonance Image (MRI) of brain is very difficult when done manually and also time consuming. Further the tumors assume different shapes and may be present in any portion of the brain. Hence identification of the tumor poses an important task in the lives of human and it is necessary to identify its exact position in the brain and the affected regions. The proposed algorithm makes use of deep learning concepts for automatic segmentation of the tumor from the MRI brain images. The algorithm is implemented using MATLAB and an accuracy of 99.1% is achieved.

Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends- A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Padmapriya Thiyagarajan ◽  
Sriramakrishnan Padmanaban ◽  
Kalaiselvi Thiruvenkadam ◽  
Somasundaram Karuppanagounder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Human Head ◽  
Medical Community ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Learning Methods ◽  
Brain Tumor Segmentation ◽  
Mri Scans

Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans. Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.

Brain Tumor Detection using Hybrid Deep Learning with Genetic Algorithm

2021 ◽  
Vol 23 (09) ◽  
pp. 981-993
Author(s):  
T. Balamurugan ◽  
◽  
E. Gnanamanoharan ◽  
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Brain Tumor ◽  
Advanced Technology ◽  
Great Promise ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Feature Subset ◽  
Brain Tumor Segmentation ◽  
Mri Image

Brain tumor segmentation is a challenging task in the medical diagnosis. The primary aim of brain tumor segmentation is to produce precise characterizations of brain tumor areas using adequately placed masks. Deep learning techniques have shown great promise in recent years for solving various computer vision problems such as object detection, image classification, and semantic segmentation. Numerous deep learning-based approaches have been implemented to achieve excellent system performance in brain tumor segmentation. This article aims to comprehensively study the recently developed brain tumor segmentation technology based on deep learning in light of the most advanced technology and its performance. A genetic algorithm based on fuzzy C-means (FCM-GA) was used in this study to segment tumor regions from brain images. The input image is scaled to 256×256 during the preprocessing stage. FCM-GA segmented a preprocessed MRI image. This is a versatile advanced machine learning (ML) technique for locating objects in large datasets. The segmented image is then subjected to hybrid feature extraction (HFE) to improve the feature subset. To obtain the best feature value, Kernel Nearest Neighbor with a genetic algorithm (KNN-GA) is used in the feature selection process. The best feature value is fed into the RESNET classifier, which divides the MRI image into meningioma, glioma, and pituitary gland regions. Real-time data sets are used to validate the performance of the proposed hybrid method. The proposed method improves average classification accuracy by 7.99 % to existing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) classification algorithms

scholarly journals An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wentao Wu ◽  
Daning Li ◽  
Jiaoyang Du ◽  
Xiangyu Gao ◽  
Wen Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Segmentation ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

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