Glioma detection on brain MRIs using texture and morphological features with ensemble learning

Gupta, Nidhi; Bhatele, Pushpraj; Khanna, Pritee

doi:10.1016/j.bspc.2018.06.003

Cited by 76 publications

(25 citation statements)

References 29 publications

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“…Using 30 texture features they reached an average classification accuracy of 96.8%. Also, Gupta et al [20] analyzed conventional MRI (T1-weighted images before and after contrast-enhancement, T2-weighted and FLAIR images) of 80 LGGs and 120 HGGs to perform three tasks: detection, location and identification of gliomas. For the third task (identification), they used geometric parameters such as area, solidity, perimeter and orientation of the tumor, in addition to consultations with radiologists.…”

Section: Plos Onementioning

confidence: 99%

“…To date, various computational methodologies have been developed for the classification of gliomas. Some of them study conventional (anatomical) [7,20] or advanced MRI (perfusion or diffusion weighted imaging, spectroscopy, etc.) [5,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are quantitative analytical methodologies that allow measuring variables from all MRI. One of the most-used is texture analysis [20,23,24], wich consists of quantifying the spatial distribution of pixels (2D images) or voxels (3D images) with different gray levels intensities, and extracting information through statistical variables (for example, correlation, homogeneity, contrast, entropy, etc.) [25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Accuracy 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 of MRI contrasts (T 1Gd and T 2) 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-sampling 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, and the best model 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 T 1Gd images, and LGGs had a more heterogeneous texture than HGGs in the T 2 images. These novel results partially contrast with results from the literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that the heterogeneity of gliomas depended on the MRI contrast studied. The chosen model stands out as a simple, low-cost, easy-to-implement, reproducible and highly accurate glioma classifier. Importantly, it should be accessible to populations with reduced economic and scientific resources.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Feature extraction is the process of transforming the raw pixel values from an image into a set of features, normally distinctive properties of input patterns that can be used in the selection and classification tasks. Feature extraction techniques are usually divided into the geometrical, statistical, model-based, and signal processing [14,16,18,42]. This stage involves obtaining important features extracted from MR images.…”

Section: Slantlet Transform (Slt)mentioning

confidence: 99%

“…In the method, feature selection using PCA and DNN models was used for brain MRI classification into normal and three categories of malignant brain tumors. Gupta et al [16] proposed a noninvasive system for brain glioma detection on brain MRIs using texture and morphological features with ensemble learning. Simulations were scored 97.37% and 98.38 on JMCD and BraTS, respectively.…”

Section: Introductionmentioning

confidence: 99%

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

Wady

Yousif

Hasan

2020

BioMed Research International

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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.

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Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification

Saravanan,

Indragandhi,

Elakkiya

et al. 2024

Cognitive Analytics and Reinforcement Learning

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Glioma detection on brain MRIs using texture and morphological features with ensemble learning

Cited by 76 publications

References 29 publications

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

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

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

Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification

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