About GlioMap
GlioMap is an innovative open-source graphical user interface (GUI) tool specifically designed to assist researchers, radiologists, and medical professionals in the comprehensive analysis of brain gliomas using advanced MRI imaging techniques. Our software integrates cutting-edge radiomic and morphological feature extraction methodologies with intuitive visualization tools and robust manual segmentation support.
Developed entirely in MATLAB, GlioMap provides seamless support for industry-standard NIfTI and DICOM image formats. The platform's sophisticated architecture enables automated tumor segmentation, comprehensive radiomic feature computation, and interactive region-of-interest (ROI) analysis, establishing it as an indispensable tool for both clinical glioma research and diagnostic applications.
This project represents a significant academic and research initiative, with the primary objective of democratizing access to efficient, reliable, and scientifically validated tools within the medical imaging and radiology community. By providing open-source access, we aim to accelerate research progress and improve patient outcomes worldwide.
Key Capabilities
Advanced Image Processing
Comprehensive preprocessing including skull stripping, bias field correction, and normalization algorithms.
Automated Segmentation
Fuzzy C-means clustering and BET-based algorithms for precise tumor boundary detection.
Radiomic Analysis
Extraction of first-order, second-order, and higher-order texture features for quantitative analysis.
Morphological Features
Comprehensive shape and geometric analysis including volume, surface area, and complexity metrics.
Interactive Visualization
Real-time slice navigation, contrast adjustment, and multi-planar reconstruction capabilities.
ROI Management
Advanced region-of-interest tools for manual refinement and quality control of segmentations.
Technical Specifications
- Platform: MATLAB (R2019b or later recommended)
- Supported Formats: NIfTI (.nii, .nii.gz), DICOM (.dcm), and multiple other medical imaging formats
- Image Types: T1, T2, FLAIR, T1-contrast enhanced, DWI, and multi-parametric MRI
- Output Formats: CSV, Excel, MAT files for feature data; NIfTI for processed images
- Operating Systems: Windows, macOS, Linux
- Memory Requirements: Minimum 8GB RAM (16GB recommended for large datasets)
Our Mission
To advance the field of medical imaging by providing accessible, validated, and comprehensive analysis tools that bridge the gap between cutting-edge research and clinical practice. We believe in the power of open-source collaboration to accelerate scientific discovery and improve patient care in neuro-oncology.