Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images

The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.

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Evaluating the accuracy of geometrical distortion correction of magnetic resonance images for use in intracranial brain tumor radiotherapy

Reports of Practical Oncology & Radiotherapy ◽

10.1016/j.rpor.2019.09.011 ◽

2019 ◽

Vol 24 (6) ◽

pp. 606-613 ◽

Cited By ~ 1

Author(s):

Seyed Mehdi Bagherimofidi ◽

Claus Chunli Yang ◽

Roberto Rey-Dios ◽

Madhava R. Kanakamedala ◽

Ali Fatemi

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Magnetic Resonance Images ◽

Distortion Correction ◽

Geometrical Distortion

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Hybrid approach for brain tumor detection and classification in magnetic resonance images

2015 Communication, Control and Intelligent Systems (CCIS) ◽

10.1109/ccintels.2015.7437900 ◽

2015 ◽

Cited By ~ 3

Author(s):

Praveen G.B. ◽

Anita Agrawal

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Hybrid Approach ◽

Tumor Detection ◽

Magnetic Resonance Images

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Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

The Scientific World JOURNAL ◽

10.1155/2015/184350 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

R. Rajesh Sharma ◽

P. Marikkannu

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Gravitational Search Algorithm ◽

Three Dimensional ◽

Magnetic Resonance Images ◽

Tumor Classification ◽

Classification Model ◽

Support Vector ◽

Preliminary Evaluation ◽

Distribution Method

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, andk-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

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Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

Traitement du signal ◽

10.18280/ts.380428 ◽

2021 ◽

Vol 38 (4) ◽

pp. 1171-1179

Author(s):

Swaraja Kuraparthi ◽

Madhavi K. Reddy ◽

C.N. Sujatha ◽

Himabindu Valiveti ◽

Chaitanya Duggineni ◽

...

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Magnetic Resonance ◽

Data Augmentation ◽

Magnetic Resonance Images ◽

Tumor Classification ◽

Support Vector ◽

Svm Classifier ◽

Tumor Type

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

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