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A CT medical image data enhancement method for cholelithiasis based on lightweight convolution neural network

A convolutional neural network and medical image technology, applied in the field of image processing, can solve problems such as undetected cholelithiasis CT medical images, achieve good visual effects and medical effects, improve effectiveness, and improve fitting capabilities

Pending Publication Date: 2019-03-29
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

At present, no lightweight convolutional neural network processing method for cholelithiasis CT medical images has been found in the market

Method used

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  • A CT medical image data enhancement method for cholelithiasis based on lightweight convolution neural network

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Embodiment Construction

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

[0031] Such as figure 1 As shown, the specific situation of the CT medical image data enhancement method for gallstone disease based on the lightweight convolutional neural network in this embodiment is as follows:

[0032] 1) Select 200 CT medical images of cholelithiasis as the initial training set of CT medical image data enhancement of cholelithiasis based on deep learning, and then improve the training set. The specific steps are:

[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 the images in the training set;

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

[0037] Performing a translation ...

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Abstract

The invention provides a CT medical image data enhancement method for cholelithiasis based on a lightweight convolution neural network, which includes constructing CT medical image data enhancement convolution neural network for cholelithiasis, wherein 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, perform stretch contrast, equalize histogram and reconstruct image. the Convolution neural network model which can be used to enhance CT medical images of cholelithiasis can be generated. The method can realizereal-time enhancement of cholelithiasis CT medical image data, and achieve good visual and medical effects.

Description

Technical field [0001] The invention relates to the field of image processing, in particular to a cholelithiasis CT medical image data enhancement method based on a lightweight convolutional neural network. Background technique [0002] The process of image recognition includes preprocessing, feature extraction, feature matching, and similarity calculation. An important part of the preprocessing is the processing of image enhancement. Its purpose is to enhance the useful information in the image to improve the visual effect of the image, and is aimed at the application of a given image. Image data enhancement is a very important part, and its processing effect directly affects the subsequent image recognition process. [0003] The gallstone disease involved in the present invention is a common digestive system disease, with various diseases, complicated pathogenic factors, high morbidity, and difficulty in dissolving and expelling stones. In addition, there are many types and for...

Claims

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Application Information

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IPC IPC(8): G06T7/136
CPCG06T7/136G06T2207/10081G06T2207/20081G06T2207/20084
Inventor 庞善臣王硕江璟瑜谢鹏飞
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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