Brain Tumor Type Detection Using Texture Features in MR Images

2018 ◽  
Author(s):  
Yogita K Dubey ◽  
Milind M Mushrif ◽  
Komal Pisar
Keyword(s):  
Brain Tumor ◽  
Texture Features ◽  
Tumor Type ◽  
Mr Images

scholarly journals Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

2019 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Sahil S. Nalawade ◽  
Gowtham K. Murugesan ◽  
Ben Wagner ◽  
Marco C. Pinho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Texture Features ◽  
Response To Therapy ◽  
Validation Dataset ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Accuracy Score ◽  
Mr Images ◽  
Brain Tumor Segmentation

ABSTRACTTumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented. The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

scholarly journals Brain Tumor Segmentation and Classification using Hybrid clustering technique and SVM Classifier

2019 ◽  
Vol 9 (1S) ◽  
pp. 14-17
Keyword(s):  
Brain Tumor ◽  
Texture Features ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Primary Concern ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Clustering Technique ◽  
Detection And Identification

Brain tumor Detection is a primary concern in today’s life. So a computer aided technology must be implemented for an accurate detection and identification of brain tumor. The tumor can be detected using various classification techniques from brain MR Images. In this paper segmentation process is being done using K means Clustering technique and Binary Thresholding, the features from the images are then extracted using GLCM where six texture features are extracted and SVM Classifier is being used for classification of the images. This proposed method shows an accuracy of 97.12%.

CLASSIFICATION OF BRAIN MRI IMAGES BY USING THE AUTOMATIC SEGMENTATION AND TEXTURE ANALYSIS

2020 ◽  
Vol 3 (4) ◽  
pp. 263-275
Author(s):  
Anastasia Viktorivna Karliuk ◽  
Ievgen Arnoldovich Nastenko ◽  
Olena Kostiantinivna Nosovets ◽  
Vitalii Olegovich Babenko
Keyword(s):  
Brain Tumor ◽  
Texture Analysis ◽  
Pituitary Tumor ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Magnetic Resonance Tomography ◽  
Texture Features ◽  
Classification Model ◽  
Tumor Type ◽  
Type Definition

Brain tumor is a relatively severe human disease type. Its timely diagnosis and tumor type definition are an actual task in modern medicine. Lately, the segmentation methods on 3D brain images (like computer and magnetic resonance tomography) are used for definition of a certain tumor type. Nevertheless, the segmentation is usually conducted manually, which requires a lot of time and depends on the experience of a doctor. This paper looks at the possibility of creating a method for the automatic segmentation of images. As a training sample, the medical database of MRI brain tomography with three tumor types (meningioma, glioma, and pituitary tumor) was taken. Taking into account the different slices, the base had: 708 examples of meningioma, 1426 examples of glioma, and 930 examples of pituitary tumor. The database authors marked the regions of interest on each image, which were used as a tutor (supervised learning) for automatic segmentation model. Before model creation, currently existing popular automatic segmentation models were analyzed. U-Net deep convolution neural network architecture was used as the most suitable one. As the result of its use, the model was obtained, which can segment the image correctly in seventy four percent of six hundred images (testing sample). After obtaining the automatic segmentation model, the Random Forest models for three “One versus All” tasks and one multiclass task were created for brain tumor classification. The total sample was divided into training (70 %), testing (20 %), and examining (10 %) ones before creating the models. The accuracy of the models in the examining sample varies from 84 to 94 percent. For model classification creation, the texture features were used, obtained by texture analysis method, and created by the co-authors of the Department of Biomedical Cybernetics in the task of liver ultrasound image classification. They were compared with well-known Haralick texture features. The comparison showed that the best way to achieve an accurate classification model is to combine all the features into one stack

scholarly journals Combining Wavelet Statistical Texture And Recurrent Neural Network For Tumour Detection And Classification Over MRI

2019 ◽  
Vol 8 (6) ◽  
pp. 3769-3778
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Recurrent Neural Network ◽  
Detection System ◽  
Region Growing ◽  
Texture Features ◽  
Noise Removal ◽  
The Body ◽  
Mr Images ◽  
The Brain

Brain tumor is one of the major causes of death among other types of the cancer because Brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper we have introduced brain tumor detection system based on combining wavelet statistical texture features and recurrent neural network (RNN). Basically, the system consists of four phases such as (i) feature extraction (ii) feature selection (iii) classification and (iii) segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on sparse principle component analysis (SPCA) approach. The next step is to classify the brain image using Recurrent Neural Network (RNN). After classification, proposed system extracts tumor region from MRI images using modified region growing segmentation algorithm (MRG). This technique has been tested against the datasets of different patients received from muthu neuro center hospital. The experimentation result proves that the proposed system achieves the better result compared to the existing approaches

scholarly journals Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Pyo Kim ◽  
Jonghoon Kim ◽  
Hyemin Jang ◽  
Jaeho Kim ◽  
Sung Hoon Kang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apoe Genotype ◽  
Texture Features ◽  
Structural Mri ◽  
Regularized Regression ◽  
Mr Images ◽  
Baseline Model ◽  
Imaging Model ◽  
T1 And T2

AbstractPredicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

Content-based brain tumor retrieval for MR images with joint deep and handcrafted visual features

2021 ◽  
Author(s):  
Weikang Huang ◽  
Wei Zou ◽  
Erxi Fang ◽  
Nan Hu ◽  
Jiajun Wang
Keyword(s):  
Brain Tumor ◽  
Visual Features ◽  
Mr Images
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