CT adaptive three-dimensional light field display method and system based on local contrast

By employing a local contrast adaptive 3D light field display method, and utilizing multi-scale gradient analysis and morphological processing, significant details in CT images are automatically enhanced. This solves the problem of significant feature occlusion in traditional 3D reconstruction of CT images, improving the visualization effect and clinical applicability of medical images.

CN121725147APending Publication Date: 2026-03-24BEIJING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-24

AI Technical Summary

Technical Problem

Traditional 3D reconstruction methods for CT images struggle to significantly mask features under global parameter settings, leading to a loss of key information. Existing enhancement methods rely on manual adjustments and are inefficient, failing to effectively distinguish structural boundaries of different natures.

Method used

A CT adaptive 3D light field display method based on local contrast is adopted. Through multi-scale gradient analysis, morphological processing and anatomical prior knowledge, significant details are automatically identified and enhanced, non-critical boundaries are suppressed, a local contrast enhancement map is constructed, and the opacity is adjusted through a nonlinear fusion function.

Benefits of technology

It achieves a significant improvement in the visual visibility of the target area while maintaining the overall rendering effect, providing a rich and detailed 3D model to assist doctors in making more accurate diagnoses and planning decisions.

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Abstract

The invention discloses a CT self-adaptive three-dimensional light field display method and system based on local contrast, and relates to the field of CT image processing and displaying, and the method comprises the steps: extracting significant features in a CT image through multi-scale gradient analysis, and combining morphological processing and / or anatomical priori knowledge to effectively distinguish a focus edge from an organ boundary, so as to improve the image quality. And therefore, an enhanced graph based on the local contrast is constructed. And finally, adaptively adjusting the opacity through a nonlinear fusion function, so that the visual visibility of the target area is remarkably improved while the overall rendering effect is kept. The method overcomes the limitation of a global transmission function, can sense the local contrast, automatically recognize and enhance significant details, inhibits non-critical boundaries, improves the visualization effect and clinical practicability of three-dimensional medical images, provides a three-dimensional model with rich levels, prominent details and depth reality for doctors, and has a good application prospect. Therefore, the system can be assisted to make more accurate diagnosis and planning decisions.
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