Finger Vein Image Processing Algorithm

This implementation processes finger vein images through the following consecutive steps: 1. Denoising: The algorithm first applies noise reduction techniques to eliminate image artifacts and enhance vein structure visibility. This typically involves filtering methods to preserve edge information while removing random noise. 2. Cropping: The region of interest (ROI) containing the finger vein pattern is extracted by removing background and unnecessary areas, focusing computational resources on relevant anatomical features. 3. Niblack Segmentation: A local thresholding technique that adapts to varying background intensities, effectively separating vein structures from surrounding tissue. This method calculates threshold values based on local mean and standard deviation within a sliding window. 4. Median Filtering: A nonlinear filtering approach that removes salt-and-pepper noise while preserving edge sharpness. This step eliminates isolated pixels and small artifacts from the segmented image. 5. Thinning: A morphological operation that reduces vein patterns to single-pixel width skeletons while maintaining topological structure. This prepares the image for feature extraction and matching algorithms. Through this multi-stage processing pipeline, the algorithm produces refined finger vein images with enhanced contrast and clear vascular patterns, suitable for biometric authentication systems.