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CT image liver segmentation method and system based on characteristic learning

A CT image and feature learning technology, applied in the field of machine learning, can solve problems such as difficult liver segmentation and poor segmentation effect, and achieve the effect of improving segmentation accuracy

Active Publication Date: 2016-08-24
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0003] The existing random walk segmentation method has the advantages of being fast and simple, but it is not effective in segmenting areas with low contrast in CT images, especially the junctions between the liver and adjacent organs such as large blood vessels and stomach, which rely solely on the gray value Difficult to efficiently segment the liver

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  • CT image liver segmentation method and system based on characteristic learning
  • CT image liver segmentation method and system based on characteristic learning
  • CT image liver segmentation method and system based on characteristic learning

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Embodiment Construction

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are the Some, but not all, embodiments are invented. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0021] Such as figure 1 As shown, the present embodiment discloses a feature learning-based CT image liver segmentation method, including:

[0022] S101. Read the training image set and the image to be segmented, wherein the training image in the training image set and the image to be segmented are CT images of the abdomen;

[0023] S102. Extracting Haar features, loc...

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Abstract

The invention discloses a CT image liver segmentation method and system based on characteristic learning, being able to effectively improve the segmentation precision of the liver in a CT image. The method comprises the steps: S101: reading a training image set and an image to be segmented, wherein the training images in the training image set and the image to be segmented are the CT images of a belly; S102: extracting the Haar characteristic of the training images and the image to be segmented, the local binary pattern characteristic, the directional gradient histogram and the co-occurrence matrix characteristic; S103: utilizing a principal component analysis method to perform characteristic fusion on all the extracted characteristics so as to acquire more effective characteristics; S104: utilizing a classifier to classify the characteristics of each pixel of the image to be segmented to obtain a liver probability graph; and S105: combining the liver probability graph with the image to be segmented, modifying the graph model weight of a random walk segmentation algorithm to realize segmentation of the liver.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a method and system for liver segmentation of CT images based on feature learning. Background technique [0002] Medical image segmentation assists doctors in identifying patients' internal tissues, organs and lesion areas, and plays a vital role in computer-aided treatment and surgical planning. Therefore, the automatic segmentation of the liver is the basis for doctors to diagnose and treat liver diseases such as cirrhosis, liver tumors, and liver transplantation. In abdominal CT images, the gray value difference between the liver and adjacent organs is small, and the liver itself has uneven gray levels and different shapes, so it is difficult to automatically and accurately segment the liver. Therefore, clinicians urgently need a simple, fast and accurate liver segmentation method. [0003] The existing random walk segmentation method has the advantages of being fas...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T2207/30056
Inventor 艾丹妮杨健王涌天丛伟建张盼
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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