Research on Human Brain Image Segmentation Technology

The human brain image segmentation process involves the following computational stages:

a. Initially, raw 2D medical images undergo filtering preprocessing (typically using Gaussian or median filters) to reduce noise, followed by volumetric reconstruction where sequential 2D slices are stacked and interpolated to form coherent 3D volume data structures suitable for三维 processing.

b. Subsequently, multi-threshold segmentation algorithms (such as Otsu's method or iterative threshold selection) are employed to determine optimal dual threshold values. These thresholds enable binarization of the 3D volume data, where voxel intensities are classified as either brain tissue or background through logical operations, achieving preliminary brain image segmentation.

c. The resulting binary 3D brain volume undergoes mathematical morphology operations - primarily erosion and dilation using structuring elements to smooth boundaries and remove artifacts. Seed-filling algorithms then perform region growing from designated seed points to refine connected components, eliminating isolated noise regions. This processed volume yields optimized 3D brain segmentation results that can be visualized using volume rendering or isosurface extraction techniques for intuitive observation.

Through this pipeline, effective human brain image segmentation is achieved, providing foundational data for subsequent neurological research and clinical applications including tumor detection and anatomical analysis.