A Vascular Lumen Segmentation Method Based on Intravascular Ultrasound Images

Abstract

The invention discloses a blood vessel lumen segmentation method based on intravascular ultrasound images. The method comprises the steps of determining seed points of a blood vessel lumen (area contained by blood vessel intima), utilizing a fuzzy connectedness algorithm to calculate fuzzy connectedness strength of each pixel point in an image and seed points of the blood vessel lumen and obtaining fuzzy connectedness strength images, and determining fuzzy connectedness strength threshold value by utilizing ultrasonic image gradient information and determining blood vessel lumen boundary according to the fuzzy connectedness strength threshold value and the fuzzy connectedness strength images. By automatically determining the seed points and fuzzy connectedness strength threshold value, the method guarantees automaticity of the segmentation process. Based on the processing method of the fuzzy connectedness algorithm, the method not only ensures a simple and effective segmentation method, but also prevents complexity of a conventional algorithm model and dependence on imaging conditions.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a fuzzy connectedness (FuzzyConnectedness) algorithm and a vascular lumen segmentation method applied to intravascular ultrasound (IVUS: Intravascular ultrasound) images. Background technique [0002] Intravascular Ultrasound (IVUS: Intravascular Ultrasound) images can not only show the shape of the lumen of the blood vessel, but also show the layered structure of the blood vessel wall, which is of great value in the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. Diagnosis of atherosclerosis based on IVUS images requires quantitative indicators of atherosclerotic image features 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 the lumen of blood vessels, the boundaries of media and advent...

AI Technical Summary

Problems solved by technology

At present, there are three main types of computerized automatic segmentation algorithms for vascular lumens based on IVUS images: the first type is statistical methods (G. Mendizabal-Ruiz, M. Rivera, et al., “A probabilistic segmentation 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 of the gray distribution of images to achieve IVUS image segmentation, but the artifacts, calcifications and other complexities in IVUS images image features will greatly reduce the accuracy of statistical modeling; the second category is the method of machine learning (1.E. G. Bovenkamp, ​​J. Dijkstra, J. G.Bosch, et al., “Multi-agent segmentation of IVUS images”, Patten Recognition , 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 ultrasounds”, IEEE Trans. On information technology in biomedicine, Vol.12, No.3, pp.335-346, 2008.), this type of method has a complex model and is subject to many restrictions in practical application; the third type is the method based on the active contour model (1. Zhang Qi, Wang Yuanyuan, etc., "Active Contour Model and Contourlet Multi-resolution Analysis to Segment Intravascular Ultrasound Images", Optical Precision Engineering, Vol.16, No.11, pp.2301-311, 2008; 2. X . Zhu, P. Zhang, J. Shao, et al., “A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation”, Ultrasonics, Vol.51, pp.181-189, 201 1.), this type of method is simple and easy to implement, but it often needs to give an initial contour line, and the segmentation result is easily affected by complex image features such as noise

Images