Three-dimensional reconstruction of CT image features based on multi-threaded deep learning calculation

Traditional technology uses serial processing method in CT image feature extraction. It is prone to loss of image data, which causes problems such as ring distortion of the reconstructed image and long reconstruction time. Therefore, a three-dimensional (3D) reconstruction algorithm for CT image features based on multi-threaded deep learning calculation is designed. Feature image textures are segmented using parameters such as gray, mean, and variance. Fusion calculates co-occurrence logarithm of the gray level with segmented sub-block and its pixels. Co-occurrence probability of the sub-block gray levels is obtained, followed by getting optimal feature volume data, which is stored in 3D texture and 1D texture for interpolation calculation.The optimal feature volume data is stored in 3D and 1D texture for interpolation calculation. Rotation matrix is stored in the global storage space of CUDA, which is used for multi-threaded calculations. After completing multi-threaded batch CT image hardware configuration and algorithm flow settings, the thread's index and bounding box is calculated. 3D reconstruction of CT image features is achieved by model accumulation. The experimental simulation proves that local detail information loss is small after reconstruction of the proposed method. Its reconstruction time is short and has good applicability.