Human tissue and organ three-dimensional reconstruction method based on structural similarity level set algorithm

Abstract

According to the human tissue and organ three-dimensional reconstruction method based on the structural similarity level set algorithm, the level set algorithm is improved according to the structuralsimilarity of continuous medical images, the segmentation result is used for pancreas three-dimensional reconstruction, and a good reconstruction effect is achieved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for three-dimensional reconstruction of human tissues and organs based on a structural similarity level set algorithm. Background technique [0002] Medical imaging refers to the technology and processing process of obtaining internal tissue and organ images of the human body or a certain part of the human body in a non-invasive manner for the purpose of medical treatment or medical research. According to the different implementation steps, medical imaging includes medical imaging technology and medical processing technology , With the help of medical imaging technology, medical personnel can more clearly understand the specific tissue and organ conditions of the human body and then give more accurate and reasonable diagnosis and treatment plans. [0003] The current existing technology has been able to extract specific tissues and organs from medical im...

AI Technical Summary

Problems solved by technology

[0004] However, since the level set algorithm is a semi-automatic segmentation algorithm, there is a problem of low segmentation efficiency when applied to continuous and large number of medical images

Although the prior art CN104599270B "A Method for Breast Tumor Ultrasonic Image Segmentation Based on Improved Level Set Algorithm" is also committed to improving the accuracy and speed of breast tumor segmentation and 3D reconstruction, the key point of this scheme is to use adaptive prefabricated segmentation The algorithm extracts the seed points without artificially setting the seed points, which simplifies the steps of manual operation. This solution still does not solve the problem of repeated operations on each image when processing multiple medical images, and does not perform well in multi-image processing.

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