Three-dimensional image visualization method based on two-dimensional transfer function

Abstract

The invention discloses a three-dimensional image visualization method based on a two-dimensional transfer function, which comprises the steps of: firstly, establishing a generalized model of the two-dimensional transfer function, and designing four classifiers; secondly, designing a concrete model of the two-dimensional transfer function; and thirdly, drawing a three-dimensional image according to optical parameters obtained by the two-dimensional transfer function. The method is simple, has popularization on a volume rendering algorithm based on graphics hardware and a volume rendering algorithm based on software, is visual in results, and can realize optical parameter set, user interest structure extraction, independence structure inhibition and three-dimensional image semitransparent rendering in volume data.

Description

3D Image Visualization Method Based on 2D Transfer Function technical field The invention relates to the field of three-dimensional visualization in computer graphics, in particular to two-dimensional transfer function technology in volume rendering. Background technique Volume Rendering appeared in the 1970s and is a very important method in 3D image visualization. Because volume rendering can not only display the surface information of volume data, but also display its internal information, so as to realize the realistic display of volume data, which is conducive to the comprehensive understanding and analysis of volume data information, so it is used in auxiliary doctor diagnosis, industrial CT defect detection. etc. play a very important role. One of the keys to the volume rendering algorithm is the transfer function (TransferFunction). The transfer function is responsible for setting the optical parameters such as color, opacity (also known as opacity) of volume da...

AI Technical Summary

Problems solved by technology

In order to solve the technical problems of setting optical parameters (such as opacity, color, etc.) of volume data, extracting structures of interest to users, suppressing irre

Images