Liver blood vessel segmentation system based on Hessian matrix and gray scale method

Abstract

The invention provides a liver blood vessel segmentation system based on a Hessian matrix and a gray scale method. The liver blood vessel segmentation system comprises an image acquisition and preprocessing unit, a blood vessel image enhancement unit, a portal vein center line extraction unit, a liver blood vessel segmentation unit and a data storage unit. The image acquisition and preprocessing unit is used for converting the liver CT image into an image file conforming to the gray level display range of the display device and preprocessing the image file; the blood vessel image enhancement unit is used for performing image enhancement on the preprocessed liver image to obtain a liver blood vessel enhanced image; the portal vein center line extraction unit is used for extracting and verifying a portal vein center line; the liver blood vessel segmentation unit is used for segmenting a liver blood vessel image according to the portal vein center line extracted by the portal vein centerline extraction unit; according to the liver blood vessel three-dimensional reconstruction system and method, liver blood vessels are accurately and reliably segmented and subjected to three-dimensional reconstruction, and clinicians are helped to improve the diagnosis efficiency.

Description

technical field [0001] The invention relates to the technical field of medical image analysis, in particular to a hepatic vessel segmentation system based on a Hessian matrix and a grayscale method. Background technique [0002] With the development of medical imaging technology and the emergence of various medical imaging equipment, the types and quantities of images obtained are becoming more and more diverse. The qualitative analysis of various vascular images based on the experience of doctors alone is low in diagnostic efficiency. With the help of computer image processing technology, qualitative and quantitative analysis of various blood vessel image data is carried out, and a series of valuable blood vessel parameters such as blood vessel edge, skeleton, and branch are obtained, which greatly improves the diagnostic efficiency of clinical diagnosis and treatment. Therefore, it is necessary to develop a liver vessel segmentation algorithm that can automatically segment...

AI Technical Summary

Problems solved by technology

[0002] With the development of medical imaging technology and the emergence of various medical imaging equipment, the types and quantities of images obtained are becoming more and more diverse. The qualitative analysis of various vascular images based on the experience of doctors alone is low in diagnostic efficiency.

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