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

Feng Chen, Khan Muhammad*, Shui Hua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)309-315
Number of pages7
JournalPattern Recognition Letters
Volume136
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes

Keywords

  • 3D reconstruction
  • CT image
  • Deep learning
  • Feature region
  • Fuzzy clustering
  • Multithreading

Fingerprint

Dive into the research topics of 'Three-dimensional reconstruction of CT image features based on multi-threaded deep learning calculation'. Together they form a unique fingerprint.

Cite this