A Liver Segmentation Method Based on 3D Graph Cut Algorithm

Abstract

The invention discloses a liver segmentation method based on a three-dimensional graph cut algorithm, which includes the following steps: S101, window level adjustment: adjust the window width and window level of the CT image sequence in advance, highlight the development of the liver area, and prepare the adjusted CT image Image A; S103, grayscale transformation: perform grayscale transformation processing on the obtained adjusted CT image A, retain the image of the liver region, filter out the dark tissue image, and obtain the enhanced CT image B; S105, initial mask processing : Randomly select a single slice in the abdominal CT image from the obtained enhanced CT image B, and use the GraphCut algorithm to perform liver two-dimensional segmentation on the single slice in the abdominal CT image sequence. The present invention uses a three-dimensional image cut algorithm to segment the liver region of a CT image, and according to the single liver segmentation result in the three-dimensional CT image, it can quickly and iteratively complete the liver segmentation and obtain complete liver image information, thereby facilitating subsequent reconstruction and realizing fast, Accurate and automated liver segmentation.

Description

Technical field [0001] The present invention relates to the field of medical image division processing, and Background technique [0002] Liver cancer is the most common malignant liver disease, and the mortality rate is high. Computer tomography (CT, Computertomography) is used as a non-invasive precise imaging method for liver cancer diagnosis, which not only enables the doctor to more directly, more clearly acquires the important data information of the lesion, but also causes the patient from the suffering of invasive diagnosis, but a lot The analysis of the CT image is time long, and it is possible to make a doctor to make a wrong judgment. Therefore, research on Computer Aid Diagnos, CAD) systems is very important and value for helping doctors improve the diagnostic efficiency. One of the most critical steps in the liver computer auxiliary diagnostic system, the segmentation of the liver CT image is to establish a three-dimensional model of the liver, analog liver vascular ...

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Problems solved by technology

[0004] 1. Morphological segmentation methods. For example, Lim uses multi-threshold combined with morphological filtering to extract the initial contour of the liver, and uses the gradient information and gray distribution information near the contour to obtain the final result. The disadvantage of this method is that it needs to manually set a large number of parameters. The accuracy of the segmentation results has a great influence, and this method is only suitable for images with a large grayscale difference between the liver and surrounding organs;

[0005] 2. Segmentation methods based on deformation models, such as Heimann combining deformation models and statistical priors for segmentation, but segmentation methods based on deformation models need to use a large number of liver shape pictures to train the statistical shape model to obtain the outline of the liver. This method takes a long time and the segmentation results are greatly affected by the training pictures;

[0006] 3. Neural network-based segmentation methods, such as Wang improved the fuzzy cellular neural network and applied it to liver segmentation, Zafer proposed a new supervised learning neural network ISNN (incremental supervised neural network), and applied it to Liver segmentation, etc. This method needs to artificially establish a template from the segmentation result, and the segmentation result is greatly affected by the template;

[0007] 4. Based on clustering segmentation methods, such as Liu combined K-means and SVM for liver segmentation, but the SVM algorithm is greatly affected by the initial value, and is sensitive to noise, and the stability of the algorithm is low

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