An Intravascular Ultrasound Image Segmentation Method

Abstract

An intravascular ultrasound image segmentation method includes the steps: determining seed points of blood vessel lumens and seed points of the outer wall of a blood vessel; obtaining probability images of the seed points of a first blood vessel lumen and the seed points of the outer wall of the blood vessel; obtaining a first gradient image of a intravascular ultrasound image; processing a threshold of the probability image of the seed points of the outer wall of the blood vessel to obtain a first sequence threshold image; determining an outer membrane boundary of the blood vessel according to the first sequence threshold image and the first gradient image; taking second communication area boundary pixels as seed points of a media; obtaining probability images of the seed points of a second blood vessel lumen and the seed points of the media; obtaining a second gradient image of the intravascular ultrasound image; processing a threshold of the probability image of the seed points of the second blood vessel lumen to obtain a second sequence threshold image; and determining an inner membrane boundary of the blood vessel according to the second sequence threshold image and the second gradient image. The probability of the probability images of the seed points of the blood vessel lumens in a second communication area is higher than 0.5.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a random walk (RandomWalker) algorithm-based method applied to intravascular ultrasound (IVUS: Intravascularultrasound) image segmentation. Background technique [0002] As an interventional real-time ultrasound imaging technology, intravascular ultrasound (IVUS: Intravascular Ultrasound) can not only display the shape of the lumen of the blood vessel, but also show the layered structure of the blood vessel wall, which is very useful for the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. important value. The diagnosis of atherosclerosis based on IVUS requires the acquisition of quantitative indicators of image features of atherosclerosis, such as vascular lumen area and plaque area. The accurate extraction of these quantitative indicators depends on effective image segmentation. Manual segmentation means that doctors manually delineate...

AI Technical Summary

Problems solved by technology

At present, there are three main categories of computer automatic segmentation algorithms for intravascular ultrasound images: the first category is statistical methods (G.Mendizabal-Ruiz, M.Rivera, et al., "Aprobabilisticsegmentation method for the identification of luminal borders in intravascular ultrasound images", IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008. ), statistical modeling is performed on the gray distribution of the image to achieve intravascular ultrasound image segmentation, but complex image features such as artifacts and calcifications in intravascular ultrasound images will greatly reduce the accuracy of statistical modeling; the second type through Intravascular ultrasound image segmentation by means of machine learning (1.E.G.Bovenkamp, ​​J.Dijkstra, J.G.Bosch, et al., "Multi-agentsegmentationofIVUSimages", PattenRecognition, Vol.37, No.4, pp.647-663, 2004; 2. G. Unal, S. Bucher, S. Carlier, et al., "Shape-driven segmentation of the arterial wall in intravascular ultrasound images", IEEE Trans. Oninformation technology in biomedicine, Vol.12, No.3, pp.335-346, 2008.), the model of this type of method is complex , subject to many limitations in practical application; the third category is the method based on the active contour model (1. Zhang Qi, Wang Yuanyuan et al., "Active Contour Model and Contourlet Multi-resolution Analysis for Segmenting Intravascular Ultrasound Images", Optical Precision Engineering, Vol.16, No.11, pp.2301-311, 2008; 2.X.Zhu, P.Zhang, J.Shao, et al., "Asnake-based method for segmentation of intravascular ultrasound images and its in vivo validation", Ultrasonics, Vol.51, pp.181-189 ,2011.), this kind of square often needs to give the initial contour line, and the segmentation result is easily affected by complex image features such as noise and different patches

Although the above-mentioned types of intravascular ultrasound image segmentation methods have a high degree of automation, they often need to go through a very complicated modeling process, and it is not convenient to quickly correct the results through human-computer interaction.

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