Intravascular ultrasound image based automatic adventitia detection method

Abstract

The invention discloses an intravascular ultrasound image based automatic adventitia detection method. The method includes processes: transforming an intravascular ultrasound image from rectangular coordinates to polar coordinates; determining seed points needed by a Fast Marching algorithm; determining marching speed of each pixel needed by the Fast Marching algorithm according to image grayscale and gradient; automatically detecting the adventitia by the aid of the Fast Marching algorithm. The seed points, a termination point and a valid marching speed function are detected automatically, so that automaticity in the detection process is guaranteed; by the Fast Marching algorithm based processing method, simpleness and effectiveness of the detection method are guaranteed, and complexity of an existing algorithm model and dependence on imaging conditions are avoided.

Description

[0001] technical field [0002] The present invention relates to the field of medical image processing, in particular to a Fast Marching algorithm and an automatic detection method for intima and intima of blood vessels applied to intravascular ultrasound (IVUS: Intravascular ultrasound) images. [0003] Background technique [0004] Intravascular Ultrasound (IVUS: Intravascular Ultrasound) images have very important clinical application value for 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 vessel lumen area and plaque area. The accurate extraction of these quantitative indicators depends on effective vessel edge detection. Manual detection means that doctors manually outline the lumen and adventitia boundaries of blood vessels, which is not only time-consuming and laborious, but also limited by the subjectivity of ...

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Problems solved by technology

At present, there are three main types of computer automatic detection (segmentation) algorithms for vessel edges 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.), perform statistical modeling on the gray distribution of images to achieve IVUS image segmentation, thereby detecting the edges of blood vessels, but Complex image features such as artifacts and calcifications in IVUS images will greatly reduce the accuracy of statistical modeling; the second category is machine learning methods (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 ultrasound images", IEEE Trans. On information technology in biomedicine, Vol.12, No.3, pp.335-346, 2008.), the model of this type of method is complicated, and there are many limitations in practical application; The third category 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”, Ultrasonic s, Vol.51, pp.181-189, 2011.), this type of method is simple and easy to implement, but it often needs to give the initial contour line, and the detection result is easily affected by complex image features such as noise

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