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A CT medical image data enhancement method for cholelithiasis based on depth learning

A medical image and deep learning technology, applied in the field of image processing, can solve problems such as enhanced processing methods for CT medical image data of undiscovered cholelithiasis, achieve good visual effects and medical effects, improve fitting ability, and improve reconstruction ability

Inactive Publication Date: 2019-01-29
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

At present, no data enhancement processing method for CT medical images of cholelithiasis has been found in the market

Method used

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  • A CT medical image data enhancement method for cholelithiasis based on depth learning

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with specific embodiment

[0031] Such as figure 1 As shown, the cholelithiasis CT medical image data enhancement method based on deep learning described in the present embodiment, its specific circumstances:

[0032] 1) Select 90 CT medical images of cholelithiasis as the initial training set of convolutional neural network based on deep learning-based CT medical image data enhancement of cholelithiasis, and then improve the training set. The specific steps are as follows:

[0033] 1-1) Using the set segmentation threshold, segment each CT medical image with gallstone lesions, and each image will be segmented into four image blocks of different sizes.

[0034] 1-2) Randomly adopt any one or more of the following steps to expand the above data set:

[0035] A step of enlarging or reducing images in the training set;

[0036] performing a rotation step on the images in said training set;

[0037] pe...

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Abstract

A CT medical image data enhancement method for cholelithiasis based on depth learning is provided. The method includes constructing CT medical image data enhancement convolution neural network for cholelithiasis. The network consists of four convolution units. Firstly, CT medical image dataset of cholelithiasis is constructed as the input of neural network, and the image is enhanced by edge information and redundant information is removed. Then, the image is cut according to the segmentation threshold to form many image blocks, and the dataset is expanded by scaling, rotation and translation.Convolution neural network (CNN) is trained continuously with data set, and adaptive lifting neural network (ALN) is used to extract image features, stretch contrast, equalize histogram and reconstruct image. Convolution neural network model can be used to enhance CT medical images of cholelithiasis. The method can realize real-time enhancement of cholelithiasis CT medical image data, and achievegood visual and medical effects.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for enhancing CT medical image data of cholelithiasis based on deep learning. Background technique [0002] The process of image recognition includes preprocessing, feature extraction, feature matching, similarity calculation and other links. An important link in preprocessing is image enhancement processing, which aims to enhance the useful information in the image and improve the visual effect of the image, aiming at the application occasion of the given image. Image data enhancement is a very important part, and its processing effect directly affects the subsequent image recognition process. [0003] The cholelithiasis involved in the present invention is a common digestive system disease, which has various disease types, complex pathogenic factors, high incidence rate, difficulty in dissolving and expulsing stones, and the like. In addition, there are various types ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/11G06T7/136G06T3/40G06T3/60
CPCG06T5/00G06T3/4046G06T3/60G06T7/11G06T7/136G06T2207/10081G06T2207/20081G06T2207/20084
Inventor 王硕宋弢王珣丁桐孟凡
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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