ArticleFrontiers in neuroscience2019
Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Computer simulation of tumour resection-induced brain deformation by a meshless approach.International journal for numerical methods in biomedical engineering · 2022Article
- State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods.Cancers · 2021Review
- Enhanced Region Growing for Brain Tumor MR Image Segmentation.Journal of imaging · 2021Article
- Data Augmentation for Brain-Tumor Segmentation: A Review.Frontiers in computational neuroscience · 2019Review
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
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Authors and funding
6 authors.
Funding
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Abstract
Gliomas have the highest mortality rate and prevalence among the primary brain tumors. In this study, we proposed a supervised brain tumor segmentation method which detects diverse tumoral structures of both high grade gliomas and low grade gliomas in magnetic resonance imaging (MRI) images based on two types of features, the gradient features and the context-sensitive features. Two-dimensional gradient and three-dimensional gradient information was fully utilized to capture the gradient change. Furthermore, we proposed a circular context-sensitive feature which captures context information effectively. These features, totally 62, were compressed and optimized based on an mRMR algorithm, and random forest was used to classify voxels based on the compact feature set. To overcome the class-imbalanced problem of MRI data, our model was trained on a class-balanced region of interest dataset. We evaluated the proposed method based on the 2015 Brain Tumor Segmentation Challenge database, and the experimental results show a competitive performance.
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