Processing NIfTI (ANALYZE) Magnetic Resonance Images - MATLAB Implementation

This MATLAB package is designed for processing NIfTI (ANALYZE) magnetic resonance images, providing robust functionality for reading, displaying, saving, and creating medical image data. The implementation includes core functions like load_nii() for data import and save_nii() for export operations, handling both NIfTI and ANALYZE 7.5 file formats with proper header parsing. The toolkit offers a comprehensive set of powerful utilities for NIfTI (ANALYZE) MRI processing. Using these tools, users can efficiently read volumetric data, visualize 2D/3D slices using view_nii() function, save processed images, and generate new image datasets. The package features an intuitive GUI interface with interactive controls for slice navigation and parameter adjustment, making image processing operations more accessible. With this package, users can rapidly load MRI datasets and perform various operations including intensity adjustment through histogram normalization algorithms, application of spatial filters (Gaussian, median) for image enhancement, and execution of advanced image analysis tasks like segmentation using region-growing or thresholding methods. The system supports multiple output formats (JPEG, PNG, TIFF) through MATLAB's imwrite function with appropriate data scaling for preservation of medical image quality. For both medical research and clinical applications, this package provides comprehensive tools for improved understanding and analysis of magnetic resonance images. The implementation includes specialized algorithms for 3D volume rendering, multi-planar reconstruction, and quantitative analysis functions. Whether you're a beginner or experienced professional, the package enables efficient image processing workflows through well-documented functions like make_nii() for dataset creation and crop_nii() for volume cropping operations. Experience this MATLAB package to leverage its powerful capabilities and user-friendly operations, making MRI data processing more efficient and accessible through standardized programming interfaces and modular function design.