SWI (Susceptibility Weighted Imaging) image based cerebral vein vessel segmentation method

Abstract

The invention relates to an SWI (Susceptibility Weighted Imaging) image based cerebral vein vessel segmentation method, which comprises the steps of reading each two-dimensional SWI cerebral vein vessel image through magnetic resonance equipment, adjusting the image resolution, and removing the skull and skin covering the periphery of the brain in the images; performing anisotropic filtering enhancement processing on the processed images; then performing improved 2D Hessian matrix filtering enhancement processing; removing a large area of noise caused by artifacts of the brain, segmenting out most small vein vessels, and removing the boundary contour of a brain image; reserving the small vein vessels mistakenly removed; and calculating a DSC value, a PPV, the sensitivity and a Kappa value of each segmented image, and performing optimization on a region with the segmentation effect being poor. The cerebral vein vessel segmentation method can acquire a stable result in different SWI brain images with great difference and under noise and interference conditions, the false positive rate is low, segmentation of a structure which does not belong to the vein is avoided, and requirements of medical images for the safety are met.

Description

technical field [0001] The invention relates to an image segmentation technology, in particular to a method for segmenting cerebral veins and blood vessels based on SWI images. Background technique [0002] Image segmentation is the technology and process of dividing the image into several specific regions with unique properties and proposing the target of interest. SWI ((Susceptibility weighted imaging) is to use the susceptibility weighted imaging of the phase image to perform Weighted calculations are used to obtain the anatomical structure, functional structure and pathological conditions of tissues and organs. SWI cerebral vein segmentation is to use the SWI image of the brain to segment the cerebral veins. [0003] The vessel-enhancing multi-scale filter of the Hessian matrix is ​​a technique proposed by Frangi et al. to enhance the linear structure. Since the vessel topology is a linear tree structure, its characteristics are usually expressed linearly. The basic pri...

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Problems solved by technology

[0006] Aiming at the defects in the prior art that the Hessian matrix vascular enhancement filter has a poor segmentation effect on low-contrast SWI cerebral venous blood vessel images, and artifacts and other noises have a greater influence, the problem to be solved in the present invention is to provide a method that can be used in different A SWI image-based cerebral vein vessel segmentation method that can obtain stable results in large and different SWI brain images as well as in the presence of noise and interference

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