PET-CT lung tumor segmentation method combining three dimensional graph cut algorithm with random walk algorithm

Abstract

The invention belongs to the field of biomedical image processing and specifically relates to a PET-CT lung tumor segmentation method combining a three dimensional graph cut algorithm with a random walk algorithm. The method comprises the following steps of: performing linear up-sampling on an original PET image and performing affine registration on PET and CT images; calibrating tumor seed points and non-tumor seed point; performing random walk algorithm segmentation on the PET image in combination with the tumor seed points; acquiring a foreground target area Ro completely including a target lung tumor area, using the areas, except the Ro, as a background area Rb of a non-lung tumor area; establishing gauss mixture models for the foreground area Ro and the background area Rb separately; computing energy items according to the gauss mixture models of the foreground area Ro and the background area Rb and obtaining a final segmentation result by using an graph cut algorithm. The method fully utilizes the function information and the PET image and the structure information of the CT image, enables complements between the random walk algorithm and the graph cut algorithm, and achieves an accurate lung tumor segmentation result.

Description

technical field [0001] The invention belongs to the field of biomedical image processing, and utilizes a Gaussian Mixture Models (Gaussian MixtureModels) optimized Graph Cut (GraphCut) method combined with a random walk (Randomwalk) algorithm to accurately segment lung tumors. Background technique [0002] Most lung tumors originate from the bronchial mucosal epithelium, so it is also called bronchial lung cancer. In the past 50 years, the incidence of lung cancer has increased significantly all over the world. According to statistics, in some countries in Europe and the United States and in large cities in my country, the incidence of lung cancer has ranked first among all male tumors. The treatment of lung tumors requires tumor location, size and shape analysis, and PET (Positron Emission Computed Tomography) and CT (Computed Tomography), as two quantitative molecular-structural imaging techniques, have been widely used in the analysis and diagnosis of lung tumors. PET ima...

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Problems solved by technology

[0003] However, many existing lung tumor methods at home and abroad are based on a single modality (PET or CT), such as: region growing method, LevelSet method, etc., the accuracy and reliability of the segmentation are not very high, and it cannot really be used in clinical practice. Application brings about qualitative change

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