Biological Image Segmentation Mask Alignment for Stroke Assessment

The patent segments the image processing task by automatically identifying and separating corresponding images from different series (e.g., DWI, ADC, FLAIR) based on anatomical landmarks and spatial coordinates. This segmentation allows each image pair to be processed independently and automatically, reducing the time required for manual review while maintaining diagnostic accuracy through systematic comparison of segmented image regions.

The system creates digital copies of the original biological images and applies segmentation masks to these copies rather than manipulating the original images directly. This copying approach enables repeated analysis, automatic alignment, and comparative evaluation of multiple image series without degrading the quality of the source images, thereby facilitating rapid assessment by less experienced clinicians while preserving diagnostic precision.

The system performs self-alignment by automatically generating segmentation masks from the image data itself and using these masks to identify corresponding regions across different series without requiring external reference images or manual intervention. The algorithm self-adjusts to account for variations in anatomical positioning, thereby improving image correspondence while keeping the processing complexity manageable through automated iterative refinement.

The patent employs parameter changes by adjusting segmentation threshold values, mask resolution levels, and alignment tolerances to optimize the balance between image correspondence and processing complexity. By dynamically modifying these parameters based on the specific characteristics of each patient's imaging data, the system achieves high correspondence accuracy without requiring overly complex processing procedures that would be difficult to implement or interpret.