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 A simple model for glioma grading based on texture analysis applied to conventional brain MRI

2020 ◽  
Author(s):  
José Gerardo Suárez-García ◽  
Javier Miguel Hernández-López ◽  
Eduardo Moreno-Barbosa ◽  
Benito de Celis-Alonso
Keyword(s):  
Texture Analysis ◽  
Low Cost ◽  
Brain Mri ◽  
Texture Features ◽  
Classification Model ◽  
Low Grade ◽  
Mri Contrast ◽  
Glioma Grading ◽  
Sensitivity Specificity

AbstractAccuracy of glioma grading is fundamental for the diagnosis, treatment planning and prognosis of patients. The purpose of this work was to develop a low cost and easy to implement classification model which distinguishes low grade gliomas (LGGs) from high grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. Different combinations between MRI contrasts (T1Gd and T2) and one segmented glioma region (necrotic and non-enhancing tumor core (NCR/NET)) were studied. Texture features obtained from the Gray Level Size Zone Matrix (GLSZM) were calculated. An under-samplig method was proposed to divide the data into different training subsets and subsequently extract complementary information for the creation of distinct classification models. The sensitivity, specificity and accuracy of the models were calculated. The best model was explicitly reported. The best model included only three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18% respectively. According to the features of the model, when the NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in the T1Gd images and LGGs had a more heterogeneous texture than HGGs in the T2 images. These novel results partially contrast with results from literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that heterogeneity of gliomas depended on the studied MRI contrast. The model presented stands out as a simple, low cost, easy to implement, reproducible and highly accurate glioma classifier. What is more important, it should be accessible to populations with reduced economic and scientific resources.

scholarly journals Characterizing Brain Tumor Regions Using Texture Analysis in Magnetic Resonance Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Yun Yu ◽  
Xi Wu ◽  
Jiu Chen ◽  
Gong Cheng ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Texture Features ◽  
Classification Model ◽  
Svm Classifier ◽  
Resonance Imaging ◽  
Third Order ◽  
Mri Scans

PurposeTo extract texture features from magnetic resonance imaging (MRI) scans of patients with brain tumors and use them to train a classification model for supporting an early diagnosis.MethodsTwo groups of regions (control and tumor) were selected from MRI scans of 40 patients with meningioma or glioma. These regions were analyzed to obtain texture features. Statistical analysis was conducted using SPSS (version 20.0), including the Shapiro–Wilk test and Wilcoxon signed-rank test, which were used to test significant differences in each feature between the tumor and healthy regions. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the data distribution so as to avoid tumor selection bias. The Gini impurity index in random forests (RFs) was used to select the top five out of all features. Based on the five features, three classification models were built respectively with three machine learning classifiers: RF, support vector machine (SVM), and back propagation (BP) neural network.ResultsSixteen of the 25 features were significantly different between the tumor and healthy areas. Through the Gini impurity index in RFs, standard deviation, first-order moment, variance, third-order absolute moment, and third-order central moment were selected to build the classification model. The classification model trained using the SVM classifier achieved the best performance, with sensitivity, specificity, and area under the curve of 94.04%, 92.3%, and 0.932, respectively.ConclusionTexture analysis with an SVM classifier can help differentiate between brain tumor and healthy areas with high speed and accuracy, which would facilitate its clinical application.

scholarly journals MRI Modality-based Brain Tumor Segmentation Using Deep Neural Networks

2021 ◽  
Author(s):  
Pankaj Eknath Kasar ◽  
Shivajirao M. Jadhav ◽  
Vineet Kansal
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Clinical Practice ◽  
Brain Tumors ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation

Abstract The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heterogeneity, isointense and hypointense tumor properties restrict manual segmentation in a fair period of time, thus restricting the use of reliable quantitative measures in clinical practice. In the clinical practice manual segmentation task is quite time consuming and their performance is highly depended on the operator’s experience. Accurate and automated tumor segmentation techniques are also needed; however, the severe spatial and structural heterogeneity of brain tumors makes automatic segmentation a difficult job. This paper proposes fully automatic segmentation of brain tumors using encoder-decoder based convolutional neural networks. The paper focuses on well-known semantic segmentation deep neural networks i.e., UNET and SEGNET for segmenting tumors from Brain MRI images. The networks are trained and tested using freely accessible standard dataset, with Dice Similarity Coefficient (DSC) as metric for whole predicted image i.e., including tumor and background. UNET’s average DSC on test dataset is 0.76 whereas for SEGNET we got average DSC 0.67. The evaluation of results proves that UNET is having better performance than SEGNET.

scholarly journals A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Shakhawan H. Wady ◽  
Raghad Z. Yousif ◽  
Harith R. Hasan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet

Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

Automatic MRI Brain Tumor Segmentation System Based on Localizing Active Contour Models

2011 ◽  
Vol 219-220 ◽  
pp. 1342-1346 ◽  
Author(s):  
Ying Wang ◽  
Zhi Xian Lin ◽  
Jian Guo Cao ◽  
Mao Qing Li
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models

In this paper, an automatic segmentation system was developed for MRI brain tumor. Local region-based active contour models were suitable for heterogeneous features of brain MRI image. But the models are sensitive to initial contour, which generally requires manual setting. An automatic MRI brain tumor segmentation system were developed based on localized contour models, which can identify tumor-dominant slice, set initial contour automatically and segment tumor’s contours from all MRI slices autonomously. K-means clustering and grayscale analysis were combined to identify tumor-dominant slice. Multi-threshold algorithm with the aid of erosion and dilation operators was adopted to obtain an initial contour for the tumor-dominant slice. The segmentation contour from the local active contour models was applied as initial contours of two-side neighboring slices. MRI brain tumor data were applied to validate the automatic segmentation system.

scholarly journals Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

2020 ◽  
Vol 37 (5) ◽  
pp. 865-871
Author(s):  
Putta Rama Krishnaveni ◽  
Gattim Naveen Kishore
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
The United States ◽  
Tumor Detection ◽  
Tumor Type ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Brain

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

scholarly journals Texture Analysis to Detect Cerebral Degeneration in Amyotrophic Lateral Sclerosis

2018 ◽  
Vol 45 (5) ◽  
pp. 533-539 ◽  
Author(s):  
Abdullah Ishaque ◽  
Rouzbeh Maani ◽  
Jerome Satkunam ◽  
Peter Seres ◽  
Dennell Mah ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Texture Analysis ◽  
Brain Mri ◽  
Texture Features ◽  
Visual Assessment ◽  
Image Resolution ◽  
Healthy Controls ◽  
Als Patients ◽  
Lateral Sclerosis ◽  
Cerebral Degeneration

AbstractBackgroundEvidence of cerebral degeneration is not apparent on routine brain MRI in amyotrophic lateral sclerosis (ALS). Texture analysis can detect change in images based on the statistical properties of voxel intensities. Our objective was to test the utility of texture analysis in detecting cerebral degeneration in ALS. A secondary objective was to determine whether the performance of texture analysis is dependent on image resolution.MethodsHigh-resolution (0.5×0.5 mm2 in-plane) coronal T2-weighted MRI of the brain were acquired from 12 patients with ALS and 19 healthy controls on a 4.7 Tesla MRI system. Image data sets at lower resolutions were created by down-sampling to 1×1, 2×2, 3×3, and 4×4 mm2. Texture features were extracted from a slice encompassing the corticospinal tract at the different resolutions and tested for their discriminatory power and correlations with clinical measures. Subjects were also classified by visual assessment by expert reviewers.ResultsTexture features were different between ALS patients and healthy controls at 1×1, 2×2, and 3×3 mm2 resolutions. Texture features correlated with measures of upper motor neuron function and disability. Optimal classification performance was achieved when best-performing texture features were combined with visual assessment at 2×2 mm2 resolution (0.851 area under the curve, 83% sensitivity, 79% specificity).ConclusionsTexture analysis can detect subtle abnormalities in MRI of ALS patients. The clinical yield of the method is dependent on image resolution. Texture analysis holds promise as a potential source of neuroimaging biomarkers in ALS.

scholarly journals Texture Analysis of 18F-FDG PET/CT for Differential Diagnosis Spinal Metastases

2021 ◽  
Vol 7 ◽  
Author(s):  
Xin Fan ◽  
Han Zhang ◽  
Yuzhen Yin ◽  
Jiajia Zhang ◽  
Mengdie Yang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Diagnostic Performance ◽  
Spinal Metastases ◽  
Texture Features ◽  
Classification Model ◽  
Support Vector ◽  
Kaplan Meier ◽  
Texture Parameters ◽  
Pet Ct ◽  
Fdg Pet Ct

Purpose: To evaluate the value of texture analysis for the differential diagnosis of spinal metastases and to improve the diagnostic performance of 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) for spinal metastases.Methods: This retrospective analysis of patients who underwent PET/CT between December 2015 and January 2020 at Shanghai Tenth People's Hospital due to high FDG uptake lesions in the spine included 45 cases of spinal metastases and 44 cases of benign high FDG uptake lesions in the spine. The patients were randomly divided into a training group of 65 and a test group of 24. Seventy-two PET texture features were extracted from each lesion, and the Mann-Whitney U-test was used to screen the training set for texture parameters that differed between the two groups in the presence or absence of spinal metastases. Then, the diagnostic performance of the texture parameters was screened out by receiver operating characteristic (ROC) curve analysis. Texture parameters with higher area under the curve (AUC) values than maximum standardized uptake values (SUVmax) were selected to construct classification models using logistic regression, support vector machines, and decision trees. The probability output of the model with high classification accuracy in the training set was used to compare the diagnostic performance of the classification model and SUVmax using the ROC curve. For all patients with spinal metastases, survival analysis was performed using the Kaplan-Meier method and Cox regression.Results: There were 51 texture parameters that differed meaningfully between benign and malignant lesions, of which four had higher AUC than SUVmax. The texture parameters were input to build a classification model using logistic regression, support vector machine, and decision tree. The accuracy of classification was 87.5, 83.34, and 75%, respectively. The accuracy of the manual diagnosis was 84.27%. Single-factor survival analysis using the Kaplan-Meier method showed that intensity was correlated with patient survival.Conclusion: Partial texture features showed higher diagnostic value for spinal metastases than SUVmax. The machine learning part of the model combined with the texture parameters was more accurate than manual diagnosis. Therefore, texture analysis may be useful to assist in the diagnosis of spinal metastases.

scholarly journals Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor

2019 ◽  
Author(s):  
Kavitha Prithiviraj ◽  
S Prabakaran
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Texture Analysis ◽  
Feature Vector ◽  
Correlation Energy ◽  
Texture Features ◽  
Different Types ◽  
Fuzzy C Means Clustering ◽  
The Brain ◽  
Mri Image

This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o and 135o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.

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