Extracting Invariant Image Features Using Radon Transform with Implementation Details

In image processing applications, the Radon transform serves as a powerful mathematical tool for extracting rotation-invariant features from images while enabling restoration to original orientations through inverse transformation for subsequent recognition tasks. This integral transform projects image data into a sinogram space (projection space), effectively capturing essential characteristics through line integrals along various angles. Implementation typically involves computing line integrals across multiple projection angles (commonly 0° to 180°), where each projection represents the sum of pixel intensities along parallel beam paths. Key functions in MATLAB include the `radon()` function for forward transformation and `iradon()` for inverse reconstruction using filtered backprojection algorithms. The transform inherently handles geometric shapes, edge patterns, and texture information by generating distinctive signature patterns in the projection domain that remain stable under rotational changes. For feature extraction, practitioners often analyze the sinogram's statistical properties or apply dimensionality reduction techniques to the projection data. The inverse Radon transform reconstructs images using interpolation methods (e.g., linear or spline) and Ram-Lak filters to minimize artifacts. This approach provides comprehensive image analysis capabilities, significantly enhancing processing accuracy in applications like medical imaging, industrial inspection, and pattern recognition systems by preserving structural information invariant to orientation changes.