Bone CT Edge Extraction Based on Mathematical Morphology Transformations Using Canny Operator

Bone CT edge extraction based on mathematical morphology transformations, utilizing the Canny operator for edge detection. This method processes images using mathematical morphology operations to more accurately extract edge information from bone CT images. The Canny operator is a classical edge detection algorithm that identifies strong edges in images through Gaussian filtering, gradient calculation, non-maximum suppression, and dual-threshold processing. In implementation, the mathematical morphology component typically employs erosion and dilation operations to enhance edge features and remove noise, while the Canny algorithm applies a multi-stage process: first smoothing the image with a Gaussian filter to reduce noise, then computing intensity gradients using operators like Sobel, followed by non-maximum suppression to thin edges, and finally hysteresis thresholding to detect strong edges while connecting weak edge pixels. By integrating these two approaches, we achieve more precise bone CT edge extraction results, where mathematical morphology helps preserve anatomical structures while Canny ensures accurate edge localization. The combined method is particularly effective for medical imaging applications requiring high-precision boundary detection.