Three-dimensional liver CT (computed tomography) image automatically segmenting method based on hyper voxels and graph cut algorithm

Abstract

Disclosed is a three-dimensional liver CT image automatically segmenting method based on hyper voxels and the graph cut algorithm. The method comprises analyzing a volume data histogram to adaptively enhance the image contrast; performing primary liver contour segmentation layer by layer through an adaptive threshold and an morphological method, selecting the largest liver segment to compute and extract a liver interest area; selecting seed points on the largest liver segment according to a primary liver contour, modelling foreground and background colors through a Gaussian mixed model; generating hyper voxels on the contrast-enhanced liver interest area through an SLIC (simple linear iterative clustering) algorithm, structuring an undirected weighted graph by taking the hyper voxels as the vertex, and segmenting the undirected weighted graph through the graph cut algorithm; performing postprocessing on segmentation results through a morphological algorithm. The three-dimensional liver CT image automatically segmenting method based on the hyper voxels and the graph cut algorithm can achieve rapid and accurate automatic segmentation of the liver in a three-dimensional abdominal CT image.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a three-dimensional liver CT image automatic segmentation method based on supervoxel and graph cut algorithms. Background technique [0002] Primary liver cancer is one of the most common malignant tumors in the world, with high morbidity and mortality. Computed tomography (CT) imaging technology is widely used in the diagnosis and treatment of liver cancer due to its advantages of accurate anatomical information, high resolution, short scanning time and high penetration rate. Accurate 3D segmentation of the liver is a fundamental work in computer-aided diagnosis and an important prerequisite for 3D visualization, quantitative analysis, surgical planning, etc. At present, clinical liver segmentation is generally done manually by doctors based on experience, which is not only time-consuming and laborious, but also the accuracy varies from person to person. Therefore, effi...

AI Technical Summary

Problems solved by technology

The region growing algorithm has the advantages of being fast and easy to implement, but it is easy to cause inaccurate segmentation results when the gray level of the liver tissue is uneven

Segmentation algorithms based on active contours and level sets need to provide initial contours, and the calculation is complex and the segmentation speed is slow

Although the model-based segmentation algorithm can obtain more accurate segmentation results, the generation of probability maps or statistical shape models requires a large number of training images and corresponding manual segmentation standards, and the segmentation results may be inaccurate when dealing with non-standard shape livers

The graph cut algorithm is widely used in medical image segmentation because it can obtain the global optimal solution. However, directly constructing a graph in units of voxels for cutting will lead to excessive calculation and cannot obtain satisfactory segmentation results.

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