Blood vessel segmentation method for liver CTA sequence image

Abstract

The invention discloses a blood vessel segmentation method for a liver CTA sequence image. Firstly contrast enhancement and noise smoothing preprocessing are performed on an inputted three-dimensional liver sequence image; then liver blood vessels and the boundary thereof are enhanced and blood vessel centers are thinned by adopting OOF and OFA algorithms; seed points of the blood vessel center lines are automatically searched according of the geometrical structure of the blood vessels, and the center lines of the liver blood vessels are extracted so as to construct a liver blood vessel tree; and finally the liver blood vessels are preliminarily segmented through combination of a fast marching method and corresponding blood vessel and background gray scale histograms are calculated, and accurate segmentation of the liver blood vessels is realized by adopting an image segmentation algorithm. The liver blood vessels can be effectively and accurately segmented by fully utilizing the geometrical shape and gray scale information of the blood vessels for aiming at the CTA sequence image which is low in contrast, high in noise and fuzzy in boundary. The blood vessel segmentation method for the liver CTA sequence image can be popularized to other three-dimensional blood vessel segmentation.

Description

technical field [0001] The invention belongs to the field of medical image processing, and relates to liver blood vessel enhancement, central line extraction and liver blood vessel segmentation in CTA sequence images. Background technique [0002] Liver vascular segmentation and 3D reconstruction help to accurately obtain the overall information of abdominal liver vascular tissue, which is the premise of computer-aided liver disease diagnosis and liver surgery planning. CTA (computed tomography angiography) is a non-invasive imaging technique with advantages of high density resolution and less damage to the human body, and is widely used in the assessment and diagnosis of liver diseases. Due to the complex structure of liver blood vessels, the intertwining of blood vessels, and the large differences between different individuals, the segmentation of liver blood vessels is facing great challenges. In clinical applications, in order to construct a liver vascular model, radiol...

AI Technical Summary

Problems solved by technology

Single gray-scale or gradient-based segmentation methods, such as 3D region growing, fuzzy clustering, etc., cannot effectively extract low-contrast liver vessels

In recent years, the active contour model and its hybrid model have been widely used in 3D blood vessel segmentation, but the evolution surface of this type of model is easy to cross the weak boundary of the blood vessel, resulting in serious over-segmentation, and the initial area of ​​the blood vessel needs to be provided interactively

In addition, the above methods are difficult to segment small blood vessels

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