Point reconstruction based very large-scale medical image 3D visualization method

Abstract

This invention relates to computer graphics and mode identification technique field and in detail to use point recreation for super large scale of medical three-dimensional image visualization and comprises the following steps: first to divide the cared part from the medical two-dimensional slice; second to recreate based on point to extract the cared organic three-dimensional surface and to store it in form of point; third to draw with three-dimension to display the extracted surface mold with the drawing method based on points.

Description

technical field [0001] The invention relates to the technical fields of computer graphics and pattern recognition, in particular to a method for performing three-dimensional visualization of ultra-large-scale medical images by using point reconstruction. Background technique [0002] Since the invention of X-rays, modern medical imaging equipment such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging), CR (Computer X-ray Imaging), B-ultrasound, and electronic endoscope have appeared one after another, making traditional medical diagnosis methods appear. a revolutionary change. With the development of modern computer science and technology, medical image processing has emerged as a new interdisciplinary subject, bringing a new dawn to medical diagnosis. However, the traditional way of viewing films is two-dimensional, requiring trained radiologists to view films and make judgments. With the development of computer visualization technology, three-dimensional visual...

AI Technical Summary

Problems solved by technology

However, the original MC algorithm has three serious problems: topological inconsistency, low computational efficiency, and too many output triangles, which greatly limit its practical use, and it is not suitable for large-scale data sets. real-time visualization

[0006] With the continuous advancement of medical imaging equipment technology, the spatial resolution of medical images is getting higher and higher, and the number of slices obtained is also increasing, which brings severe challenges to the existing 3D visualization algorithms.

Especially since the emergence of the virtual human body project, the amount of data obtained by the virtual human body in the United States is greater than 36GB, while the amount of data obtained by the virtual human body in China is greater than 100GB. Such a large amount of data makes real-time processing and display of 3D more difficult. , if the traditional visualization algorithm is used, tens of millions or even hundreds of millions of voxels or triangular patches will be obtained. In the current situation, even supercomputers cannot process them in real time, so developing new ones that can process them efficiently The method of massive and ultra-large-scale medical imaging data sets is particularly urgent

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