Neural network-based improved RSG liver CT image interactive segmentation algorithm

Abstract

The invention provides a one-dimensional convolutional neural network-based improved region growing algorithm for interactive segmentation of a liver CT image. Multiple kinds of information such as gray values, spatial information and different gradient values of pixels are taken into overall consideration through a neural network to serve as growth rules, so the stability of the region growth method is improved, and the segmentation capacity of the algorithm for an edge complex structure is enhanced. The method comprises the following specific steps: firstly, preprocessing an image, extracting slices containing the liver in a CT image sequence set, and converting a CT image into a grayscale image by using a window algorithm; then, carrying out image edge detection, calculating gradient values of a pixel under different edge detection operators to serve as features of the pixel in order to form a pixel feature vector; constructing a network model, extracting a training data set, and training the network model; and finally, performing segmentation, using the trained convolutional neural network model as a growth criterion of a region growth algorithm, using a mouse to click a liverregion to generate an initial segmentation result, and using a morphological method to fill holes to obtain a final result.

Description

technical field [0001] The present invention proposes an improved Region Seeds Growing algorithm (Region Seeds Growing, RSG) based on a one-dimensional convolutional neural network to interactively segment liver CT images, and consider pixel gray values, spatial information, different gradient values, etc. through the neural network A variety of information is used as the growth rule, which improves the stability of the region growing method and enhances the algorithm's ability to segment the complex structure of the edge. Background technique [0002] CT is a non-invasive imaging method of organs in vitro. Due to its fast imaging speed, high resolution and good effect, it has become an indispensable and important means for clinicians to conduct medical diagnosis. The combination of visualization technology and medical image analysis is in the It plays a dominant role in the diagnosis of liver disease. By segmenting liver CT images, extracting liver tissue and obtaining cor...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of low precision and weak stability of the traditional region growing method for liver CT image segmentation, and proposes to use the improved region growing algorithm based on one-dimensional convolutional neural network to interactively segment liver CT images. The neural network considers multiple information such as the gray value of the pixel, spatial information, and different gradient values ​​as the growth rule. In order to achieve the above object, the steps of the present invention are as follows:

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