ArticleInternational journal of biomedical imaging2015
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.
Article in International journal of biomedical imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 104 citations in OpenAlex.
- Review
- Tumor location, genomic alterations, and radiomic features as predictors of survival in glioblastoma: a Multi-Modal analysis.Neuroradiology · 2025Article
- Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.Scientific reports · 2025Article
- Machine learning and deep learning for brain tumor MRI image segmentation.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering.Sensors (Basel, Switzerland) · 2023Article
- Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.Sensors (Basel, Switzerland) · 2023Article
- Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Earlier Detection of Brain Tumor by Pre-Processing Based on Histogram Equalization with Neural Network.Healthcare (Basel, Switzerland) · 2022Article
- A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders.Biology · 2022Review
- Confidence Region Identification and Contour Detection in MRI Image.Computational intelligence and neuroscience · 2022Article
- Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI.Contrast media & molecular imaging · 2022Review
- Radiomic Features Associated with Extent of Resection in Glioma Surgery.Acta neurochirurgica. Supplement · 2022Review
- Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques.Healthcare informatics research · 2022Article
- Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.AJNR. American journal of neuroradiology · 2021Article
- Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.Brain sciences · 2020Review
- Brain Tumor Detection and Segmentation by Intensity Adjustment.Journal of medical systems · 2019Article
- Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation.Frontiers in oncology · 2019Review
- Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network.Brain informatics · 2018Article
- Computer-assisted brain tumor type discrimination using magnetic resonance imaging features.Biomedical engineering letters · 2018Review
Corrections and comments
- Erratum issued
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation with morphological operations of MR images. Multiclassifier techniques were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate GBM and normal tissue. GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy performance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy (97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy decreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising to enhance the characteristics of heterogeneity and hence early treatment of GBM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.